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2020


Label Efficient Visual Abstractions for Autonomous Driving
Label Efficient Visual Abstractions for Autonomous Driving

Behl, A., Chitta, K., Prakash, A., Ohn-Bar, E., Geiger, A.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, October 2020 (conference)

Abstract
It is well known that semantic segmentation can be used as an effective intermediate representation for learning driving policies. However, the task of street scene semantic segmentation requires expensive annotations. Furthermore, segmentation algorithms are often trained irrespective of the actual driving task, using auxiliary image-space loss functions which are not guaranteed to maximize driving metrics such as safety or distance traveled per intervention. In this work, we seek to quantify the impact of reducing segmentation annotation costs on learned behavior cloning agents. We analyze several segmentation-based intermediate representations. We use these visual abstractions to systematically study the trade-off between annotation efficiency and driving performance, ie, the types of classes labeled, the number of image samples used to learn the visual abstraction model, and their granularity (eg, object masks vs. 2D bounding boxes). Our analysis uncovers several practical insights into how segmentation-based visual abstractions can be exploited in a more label efficient manner. Surprisingly, we find that state-of-the-art driving performance can be achieved with orders of magnitude reduction in annotation cost. Beyond label efficiency, we find several additional training benefits when leveraging visual abstractions, such as a significant reduction in the variance of the learned policy when compared to state-of-the-art end-to-end driving models.

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pdf slides video Project Page [BibTex]

2020


pdf slides video Project Page [BibTex]


Convolutional Occupancy Networks
Convolutional Occupancy Networks

Peng, S., Niemeyer, M., Mescheder, L., Pollefeys, M., Geiger, A.

In European Conference on Computer Vision (ECCV), Springer International Publishing, Cham, August 2020 (inproceedings)

Abstract
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited to comparably simple geometry of single objects and do not scale to more complicated or large-scale scenes. The key limiting factor of implicit methods is their simple fully-connected network architecture which does not allow for integrating local information in the observations or incorporating inductive biases such as translational equivariance. In this paper, we propose Convolutional Occupancy Networks, a more flexible implicit representation for detailed reconstruction of objects and 3D scenes. By combining convolutional encoders with implicit occupancy decoders, our model incorporates inductive biases, enabling structured reasoning in 3D space. We investigate the effectiveness of the proposed representation by reconstructing complex geometry from noisy point clouds and low-resolution voxel representations. We empirically find that our method enables the fine-grained implicit 3D reconstruction of single objects, scales to large indoor scenes, and generalizes well from synthetic to real data.

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pdf suppmat video Project Page [BibTex]

pdf suppmat video Project Page [BibTex]


Learning Sensory-Motor Associations from Demonstration
Learning Sensory-Motor Associations from Demonstration

Berenz, V., Bjelic, A., Herath, L., Mainprice, J.

29th IEEE International Conference on Robot and Human Interactive Communication (Ro-Man 2020), August 2020 (conference) Accepted

Abstract
We propose a method which generates reactive robot behavior learned from human demonstration. In order to do so, we use the Playful programming language which is based on the reactive programming paradigm. This allows us to represent the learned behavior as a set of associations between sensor and motor primitives in a human readable script. Distinguishing between sensor and motor primitives introduces a supplementary level of granularity and more importantly enforces feedback, increasing adaptability and robustness. As the experimental section shows, useful behaviors may be learned from a single demonstration covering a very limited portion of the task space.

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[BibTex]

[BibTex]


Category Level Object Pose Estimation via Neural Analysis-by-Synthesis
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis

Chen, X., Dong, Z., Song, J., Geiger, A., Hilliges, O.

In European Conference on Computer Vision (ECCV), Springer International Publishing, Cham, August 2020 (inproceedings)

Abstract
Many object pose estimation algorithms rely on the analysis-by-synthesis framework which requires explicit representations of individual object instances. In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module that is capable of implicitly representing the appearance, shape and pose of entire object categories, thus rendering the need for explicit CAD models per object instance unnecessary. The image synthesis network is designed to efficiently span the pose configuration space so that model capacity can be used to capture the shape and local appearance (i.e., texture) variations jointly. At inference time the synthesized images are compared to the target via an appearance based loss and the error signal is backpropagated through the network to the input parameters. Keeping the network parameters fixed, this allows for iterative optimization of the object pose, shape and appearance in a joint manner and we experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone. When provided with depth measurements, to overcome scale ambiguities, the method can accurately recover the full 6DOF pose successfully.

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Project Page pdf suppmat [BibTex]

Project Page pdf suppmat [BibTex]


How to Train Your Differentiable Filter
How to Train Your Differentiable Filter

Alina Kloss, G. M. J. B.

In July 2020 (inproceedings)

Abstract
In many robotic applications, it is crucial to maintain a belief about the state of a system. These state estimates serve as input for planning and decision making and provide feedback during task execution. Recursive Bayesian Filtering algorithms address the state estimation problem, but they require models of process dynamics and sensory observations as well as noise characteristics of these models. Recently, multiple works have demonstrated that these models can be learned by end-to-end training through differentiable versions of Recursive Filtering algorithms.The aim of this work is to improve understanding and applicability of such differentiable filters (DF). We implement DFs with four different underlying filtering algorithms and compare them in extensive experiments. We find that long enough training sequences are crucial for DF performance and that modelling heteroscedastic observation noise significantly improves results. And while the different DFs perform similarly on our example task, we recommend the differentiable Extended Kalman Filter for getting started due to its simplicity.

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pdf [BibTex]


Learning of sub-optimal gait controllers for magnetic walking soft millirobots
Learning of sub-optimal gait controllers for magnetic walking soft millirobots

Culha, U., Demir, S. O., Trimpe, S., Sitti, M.

In Proceedings of Robotics: Science and Systems, 2020 (inproceedings)

Abstract
Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent stochastic variability during fabrication at the small scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of robot control is additionally crucial for medical operations, as operation environments show large variability, and robot materials may degrade or change over time,which would have deteriorating effects on the robot motion and task performance. Therefore, we propose using a probabilistic learning approach for millimeter-scale magnetic walking soft robots using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme to find controller parameters while optimizing the stride length performance of the walking soft millirobot robot within a small number of physical experiments. We demonstrate adaptation to fabrication variabilities in three different robots and to walking surfaces with different roughness. We also show an improvement in the learning performance by transferring the learning results of one robot to the others as prior information.

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link (url) DOI [BibTex]


Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image

Paschalidou, D., Gool, L., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
Humans perceive the 3D world as a set of distinct objects that are characterized by various low-level (geometry, reflectance) and high-level (connectivity, adjacency, symmetry) properties. Recent methods based on convolutional neural networks (CNNs) demonstrated impressive progress in 3D reconstruction, even when using a single 2D image as input. However, the majority of these methods focuses on recovering the local 3D geometry of an object without considering its part-based decomposition or relations between parts. We address this challenging problem by proposing a novel formulation that allows to jointly recover the geometry of a 3D object as a set of primitives as well as their latent hierarchical structure without part-level supervision. Our model recovers the higher level structural decomposition of various objects in the form of a binary tree of primitives, where simple parts are represented with fewer primitives and more complex parts are modeled with more components. Our experiments on the ShapeNet and D-FAUST datasets demonstrate that considering the organization of parts indeed facilitates reasoning about 3D geometry.

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pdf suppmat Video 2 Project Page Slides Poster Video 1 [BibTex]

pdf suppmat Video 2 Project Page Slides Poster Video 1 [BibTex]


Towards 5-DoF Control of an Untethered Magnetic Millirobot via MRI Gradient Coils
Towards 5-DoF Control of an Untethered Magnetic Millirobot via MRI Gradient Coils

Onder Erin, D. A. M. E. T., Sitti, M.

In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages: 6551-6557, 2020 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis

Schwarz, K., Liao, Y., Niemeyer, M., Geiger, A.

In Advances in Neural Information Processing Systems (NeurIPS), 2020 (inproceedings)

Abstract
While 2D generative adversarial networks have enabled high-resolution image synthesis, they largely lack an understanding of the 3D world and the image formation process. Thus, they do not provide precise control over camera viewpoint or object pose. To address this problem, several recent approaches leverage intermediate voxel-based representations in combination with differentiable rendering. However, existing methods either produce low image resolution or fall short in disentangling camera and scene properties, eg, the object identity may vary with the viewpoint. In this paper, we propose a generative model for radiance fields which have recently proven successful for novel view synthesis of a single scene. In contrast to voxel-based representations, radiance fields are not confined to a coarse discretization of the 3D space, yet allow for disentangling camera and scene properties while degrading gracefully in the presence of reconstruction ambiguity. By introducing a multi-scale patch-based discriminator, we demonstrate synthesis of high-resolution images while training our model from unposed 2D images alone. We systematically analyze our approach on several challenging synthetic and real-world datasets. Our experiments reveal that radiance fields are a powerful representation for generative image synthesis, leading to 3D consistent models that render with high fidelity.

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pdf suppmat video Project Page [BibTex]

pdf suppmat video Project Page [BibTex]


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A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models

Agudelo-España, D., Zadaianchuk, A., Wenk, P., Garg, A., Akpo, J., Grimminger, F., Viereck, J., Naveau, M., Righetti, L., Martius, G., Krause, A., Schölkopf, B., Bauer, S., Wüthrich, M.

IEEE International Conference on Robotics and Automation (ICRA), 2020 (conference) Accepted

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Project Page PDF [BibTex]

Project Page PDF [BibTex]


Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis

Liao, Y., Schwarz, K., Mescheder, L., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task.

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pdf suppmat Video 2 Project Page Video 1 Slides Poster [BibTex]

pdf suppmat Video 2 Project Page Video 1 Slides Poster [BibTex]


Exploring Data Aggregation in Policy Learning for Vision-based Urban Autonomous Driving
Exploring Data Aggregation in Policy Learning for Vision-based Urban Autonomous Driving

Prakash, A., Behl, A., Ohn-Bar, E., Chitta, K., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
Data aggregation techniques can significantly improve vision-based policy learning within a training environment, e.g., learning to drive in a specific simulation condition. However, as on-policy data is sequentially sampled and added in an iterative manner, the policy can specialize and overfit to the training conditions. For real-world applications, it is useful for the learned policy to generalize to novel scenarios that differ from the training conditions. To improve policy learning while maintaining robustness when training end-to-end driving policies, we perform an extensive analysis of data aggregation techniques in the CARLA environment. We demonstrate how the majority of them have poor generalization performance, and develop a novel approach with empirically better generalization performance compared to existing techniques. Our two key ideas are (1) to sample critical states from the collected on-policy data based on the utility they provide to the learned policy in terms of driving behavior, and (2) to incorporate a replay buffer which progressively focuses on the high uncertainty regions of the policy's state distribution. We evaluate the proposed approach on the CARLA NoCrash benchmark, focusing on the most challenging driving scenarios with dense pedestrian and vehicle traffic. Our approach improves driving success rate by 16% over state-of-the-art, achieving 87% of the expert performance while also reducing the collision rate by an order of magnitude without the use of any additional modality, auxiliary tasks, architectural modifications or reward from the environment.

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pdf suppmat Video 2 Project Page Slides Video 1 [BibTex]

pdf suppmat Video 2 Project Page Slides Video 1 [BibTex]


Learning Situational Driving
Learning Situational Driving

Ohn-Bar, E., Prakash, A., Behl, A., Chitta, K., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
Human drivers have a remarkable ability to drive in diverse visual conditions and situations, e.g., from maneuvering in rainy, limited visibility conditions with no lane markings to turning in a busy intersection while yielding to pedestrians. In contrast, we find that state-of-the-art sensorimotor driving models struggle when encountering diverse settings with varying relationships between observation and action. To generalize when making decisions across diverse conditions, humans leverage multiple types of situation-specific reasoning and learning strategies. Motivated by this observation, we develop a framework for learning a situational driving policy that effectively captures reasoning under varying types of scenarios. Our key idea is to learn a mixture model with a set of policies that can capture multiple driving modes. We first optimize the mixture model through behavior cloning, and show it to result in significant gains in terms of driving performance in diverse conditions. We then refine the model by directly optimizing for the driving task itself, i.e., supervised with the navigation task reward. Our method is more scalable than methods assuming access to privileged information, e.g., perception labels, as it only assumes demonstration and reward-based supervision. We achieve over 98% success rate on the CARLA driving benchmark as well as state-of-the-art performance on a newly introduced generalization benchmark.

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pdf suppmat Video 2 Project Page Video 1 Slides [BibTex]

pdf suppmat Video 2 Project Page Video 1 Slides [BibTex]


On Joint Estimation of Pose, Geometry and svBRDF from a Handheld Scanner
On Joint Estimation of Pose, Geometry and svBRDF from a Handheld Scanner

Schmitt, C., Donne, S., Riegler, G., Koltun, V., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
We propose a novel formulation for joint recovery of camera pose, object geometry and spatially-varying BRDF. The input to our approach is a sequence of RGB-D images captured by a mobile, hand-held scanner that actively illuminates the scene with point light sources. Compared to previous works that jointly estimate geometry and materials from a hand-held scanner, we formulate this problem using a single objective function that can be minimized using off-the-shelf gradient-based solvers. By integrating material clustering as a differentiable operation into the optimization process, we avoid pre-processing heuristics and demonstrate that our model is able to determine the correct number of specular materials independently. We provide a study on the importance of each component in our formulation and on the requirements of the initial geometry. We show that optimizing over the poses is crucial for accurately recovering fine details and that our approach naturally results in a semantically meaningful material segmentation.

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pdf Project Page Slides Video Poster [BibTex]

pdf Project Page Slides Video Poster [BibTex]


Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition

Hassan Alhaija, Siva Mustikovela, Varun Jampani, Justus Thies, Matthias Niessner, Andreas Geiger, Carsten Rother

In International Conference on 3D Vision (3DV), 2020 (inproceedings)

Abstract
Neural rendering techniques promise efficient photo-realistic image synthesis while providing rich control over scene parameters by learning the physical image formation process. While several supervised methods have been pro-posed for this task, acquiring a dataset of images with accurately aligned 3D models is very difficult. The main contribution of this work is to lift this restriction by training a neural rendering algorithm from unpaired data. We pro-pose an auto encoder for joint generation of realistic images from synthetic 3D models while simultaneously decomposing real images into their intrinsic shape and appearance properties. In contrast to a traditional graphics pipeline, our approach does not require to specify all scene properties, such as material parameters and lighting by hand.Instead, we learn photo-realistic deferred rendering from a small set of 3D models and a larger set of unaligned real images, both of which are easy to acquire in practice. Simultaneously, we obtain accurate intrinsic decompositions of real images while not requiring paired ground truth. Our experiments confirm that a joint treatment of rendering and de-composition is indeed beneficial and that our approach out-performs state-of-the-art image-to-image translation base-lines both qualitatively and quantitatively.

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pdf suppmat [BibTex]

pdf suppmat [BibTex]


Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision

Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these approaches are currently restricted to voxel- and mesh-based representations, suffering from discretization or low resolution. In this work, we propose a differentiable rendering formulation for implicit shape and texture representations. Implicit representations have recently gained popularity as they represent shape and texture continuously. Our key insight is that depth gradients can be derived analytically using the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our single-view reconstructions rival those learned with full 3D supervision. Moreover, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes.

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pdf suppmat Video 2 Project Page Video 1 Video 3 Slides Poster [BibTex]

pdf suppmat Video 2 Project Page Video 1 Video 3 Slides Poster [BibTex]


Learning Implicit Surface Light Fields
Learning Implicit Surface Light Fields

Oechsle, M., Niemeyer, M., Reiser, C., Mescheder, L., Strauss, T., Geiger, A.

In International Conference on 3D Vision (3DV), 2020 (inproceedings)

Abstract
Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to represent object appearance, photo-realistic image synthesis requires reasoning about the complex interplay of light, geometry and surface properties. In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field. In contrast to existing representations, our implicit model represents surface light fields in a continuous fashion and independent of the geometry. Moreover, we condition the surface light field with respect to the location and color of a small light source. Compared to traditional surface light field models, this allows us to manipulate the light source and relight the object using environment maps. We further demonstrate the capabilities of our model to predict the visual appearance of an unseen object from a single real RGB image and corresponding 3D shape information. As evidenced by our experiments, our model is able to infer rich visual appearance including shadows and specular reflections. Finally, we show that the proposed representation can be embedded into a variational auto-encoder for generating novel appearances that conform to the specified illumination conditions.

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pdf suppmat Project Page [BibTex]

pdf suppmat Project Page [BibTex]

2016


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Predictive and Self Triggering for Event-based State Estimation

Trimpe, S.

In Proceedings of the 55th IEEE Conference on Decision and Control (CDC), pages: 3098-3105, Las Vegas, NV, USA, December 2016 (inproceedings)

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arXiv PDF DOI Project Page [BibTex]

2016


arXiv PDF DOI Project Page [BibTex]


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Using Probabilistic Movement Primitives for Striking Movements

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 502-508, November 2016 (conference)

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis
Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis

Huang, Y., Büchler, D., Koc, O., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 650-655, November 2016 (conference)

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final link (url) DOI Project Page [BibTex]

final link (url) DOI Project Page [BibTex]


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The Role of Measurement Uncertainty in Optimal Control for Contact Interactions
Workshop on the Algorithmic Foundations of Robotics, pages: 22, November 2016 (conference)

Abstract
Stochastic Optimal Control (SOC) typically considers noise only in the process model, i.e. unknown disturbances. However, in many robotic applications that involve interaction with the environment, such as locomotion and manipulation, uncertainty also comes from lack of pre- cise knowledge of the world, which is not an actual disturbance. We de- velop a computationally efficient SOC algorithm, based on risk-sensitive control, that takes into account uncertainty in the measurements. We include the dynamics of an observer in such a way that the control law explicitly depends on the current measurement uncertainty. We show that high measurement uncertainty leads to low impedance behaviors, a result in contrast with the effects of process noise variance that creates stiff behaviors. Simulation results on a simple 2D manipulator show that our controller can create better interaction with the environment under uncertain contact locations than traditional SOC approaches.

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arXiv [BibTex]

arXiv [BibTex]


Steering control of a water-running robot using an active tail
Steering control of a water-running robot using an active tail

Kim, H., Jeong, K., Sitti, M., Seo, T.

In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, pages: 4945-4950, October 2016 (inproceedings)

Abstract
Many highly dynamic novel mobile robots have been developed being inspired by animals. In this study, we are inspired by a basilisk lizard's ability to run and steer on water surface for a hexapedal robot. The robot has an active tail with a circular plate, which the robot rotates to steer on water. We dynamically modeled the platform and conducted simulations and experiments on steering locomotion with a bang-bang controller. The robot can steer on water by rotating the tail, and the controlled steering locomotion is stable. The dynamic modelling approximates the robot's steering locomotion and the trends of the simulations and experiments are similar, although there are errors between the desired and actual angles. The robot's maneuverability on water can be improved through further research.

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DOI [BibTex]

DOI [BibTex]


Learning Where to Search Using Visual Attention
Learning Where to Search Using Visual Attention

Kloss, A., Kappler, D., Lensch, H. P. A., Butz, M. V., Schaal, S., Bohg, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, October 2016 (conference)

Abstract
One of the central tasks for a household robot is searching for specific objects. It does not only require localizing the target object but also identifying promising search locations in the scene if the target is not immediately visible. As computation time and hardware resources are usually limited in robotics, it is desirable to avoid expensive visual processing steps that are exhaustively applied over the entire image. The human visual system can quickly select those image locations that have to be processed in detail for a given task. This allows us to cope with huge amounts of information and to efficiently deploy the limited capacities of our visual system. In this paper, we therefore propose to use human fixation data to train a top-down saliency model that predicts relevant image locations when searching for specific objects. We show that the learned model can successfully prune bounding box proposals without rejecting the ground truth object locations. In this aspect, the proposed model outperforms a model that is trained only on the ground truth segmentations of the target object instead of fixation data.

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Project Page [BibTex]

PDF Project Page [BibTex]


Parameter Learning for Improving Binary Descriptor Matching
Parameter Learning for Improving Binary Descriptor Matching

Sankaran, B., Ramalingam, S., Taguchi, Y.

In International Conference on Intelligent Robots and Systems (IROS) 2016, IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2016 (inproceedings)

Abstract
Binary descriptors allow fast detection and matching algorithms in computer vision problems. Though binary descriptors can be computed at almost two orders of magnitude faster than traditional gradient based descriptors, they suffer from poor matching accuracy in challenging conditions. In this paper we propose three improvements for binary descriptors in their computation and matching that enhance their performance in comparison to traditional binary and non-binary descriptors without compromising their speed. This is achieved by learning some weights and threshold parameters that allow customized matching under some variations such as lighting and viewpoint. Our suggested improvements can be easily applied to any binary descriptor. We demonstrate our approach on the ORB (Oriented FAST and Rotated BRIEF) descriptor and compare its performance with the traditional ORB and SIFT descriptors on a wide variety of datasets. In all instances, our enhancements outperform standard ORB and is comparable to SIFT.

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[BibTex]

[BibTex]


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A New Trajectory Generation Framework in Robotic Table Tennis

Koc, O., Maeda, G., Peters, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pages: 3750-3756, October 2016 (conference)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Superpixel Convolutional Networks using Bilateral Inceptions
Superpixel Convolutional Networks using Bilateral Inceptions

Gadde, R., Jampani, V., Kiefel, M., Kappler, D., Gehler, P.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, Springer, 14th European Conference on Computer Vision, October 2016 (inproceedings)

Abstract
In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new “bilateral inception” module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception modules between the last CNN (1 × 1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.

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pdf supplementary poster Project Page Project Page [BibTex]

pdf supplementary poster Project Page Project Page [BibTex]


Barrista - Caffe Well-Served
Barrista - Caffe Well-Served

Lassner, C., Kappler, D., Kiefel, M., Gehler, P.

In ACM Multimedia Open Source Software Competition, ACM OSSC16, October 2016 (inproceedings)

Abstract
The caffe framework is one of the leading deep learning toolboxes in the machine learning and computer vision community. While it offers efficiency and configurability, it falls short of a full interface to Python. With increasingly involved procedures for training deep networks and reaching depths of hundreds of layers, creating configuration files and keeping them consistent becomes an error prone process. We introduce the barrista framework, offering full, pythonic control over caffe. It separates responsibilities and offers code to solve frequently occurring tasks for pre-processing, training and model inspection. It is compatible to all caffe versions since mid 2015 and can import and export .prototxt files. Examples are included, e.g., a deep residual network implemented in only 172 lines (for arbitrary depths), comparing to 2320 lines in the official implementation for the equivalent model.

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pdf link (url) DOI Project Page [BibTex]

pdf link (url) DOI Project Page [BibTex]


Targeting of cell mockups using sperm-shaped microrobots in vitro
Targeting of cell mockups using sperm-shaped microrobots in vitro

Khalil, I. S., Tabak, A. F., Hosney, A., Klingner, A., Shalaby, M., Abdel-Kader, R. M., Serry, M., Sitti, M.

In Biomedical Robotics and Biomechatronics (BioRob), 2016 6th IEEE International Conference on, pages: 495-501, July 2016 (inproceedings)

Abstract
Sperm-shaped microrobots are controlled under the influence of weak oscillating magnetic fields (milliTesla range) to selectively target cell mockups (i.e., gas bubbles with average diameter of 200 μm). The sperm-shaped microrobots are fabricated by electrospinning using a solution of polystyrene, dimethylformamide, and iron oxide nanoparticles. These nanoparticles are concentrated within the head of the microrobot, and hence enable directional control along external magnetic fields. The magnetic dipole moment of the microrobot is characterized (using the flip-time technique) to be 1.4×10-11 A.m2, at magnetic field of 28 mT. In addition, the morphology of the microrobot is characterized using Scanning Electron Microscopy images. The characterized parameters and morphology are used in the simulation of the locomotion mechanism of the microrobot to prove that its motion depends on breaking the time-reversal symmetry, rather than pulling with the magnetic field gradient. We experimentally demonstrate that the microrobot can controllably follow S-shaped, U-shaped, and square paths, and selectively target the cell mockups using image guidance and under the influence of the oscillating magnetic fields.

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DOI [BibTex]

DOI [BibTex]


Analysis of the magnetic torque on a tilted permanent magnet for drug delivery in capsule robots
Analysis of the magnetic torque on a tilted permanent magnet for drug delivery in capsule robots

Munoz, F., Alici, G., Zhou, H., Li, W., Sitti, M.

In Advanced Intelligent Mechatronics (AIM), 2016 IEEE International Conference on, pages: 1386-1391, July 2016 (inproceedings)

Abstract
In this paper, we present the analysis of the torque transmitted to a tilted permanent magnet that is to be embedded in a capsule robot to achieve targeted drug delivery. This analysis is carried out by using an analytical model and experimental results for a small cubic permanent magnet that is driven by an external magnetic system made of an array of arc-shaped permanent magnets (ASMs). Our experimental results, which are in agreement with the analytical results, show that the cubic permanent magnet can safely be actuated for inclinations lower than 75° without having to make positional adjustments in the external magnetic system. We have found that with further inclinations, the cubic permanent magnet to be embedded in a drug delivery mechanism may stall. When it stalls, the external magnetic system's position and orientation would have to be adjusted to actuate the cubic permanent magnet and the drug release mechanism. This analysis of the transmitted torque is helpful for the development of real-time control strategies for magnetically articulated devices.

pi

DOI [BibTex]

DOI [BibTex]


Robust Gaussian Filtering using a Pseudo Measurement
Robust Gaussian Filtering using a Pseudo Measurement

Wüthrich, M., Garcia Cifuentes, C., Trimpe, S., Meier, F., Bohg, J., Issac, J., Schaal, S.

In Proceedings of the American Control Conference (ACC), Boston, MA, USA, July 2016 (inproceedings)

Abstract
Most widely-used state estimation algorithms, such as the Extended Kalman Filter and the Unscented Kalman Filter, belong to the family of Gaussian Filters (GF). Unfortunately, GFs fail if the measurement process is modelled by a fat-tailed distribution. This is a severe limitation, because thin-tailed measurement models, such as the analytically-convenient and therefore widely-used Gaussian distribution, are sensitive to outliers. In this paper, we show that mapping the measurements into a specific feature space enables any existing GF algorithm to work with fat-tailed measurement models. We find a feature function which is optimal under certain conditions. Simulation results show that the proposed method allows for robust filtering in both linear and nonlinear systems with measurements contaminated by fat-tailed noise.

am ics

Web link (url) DOI Project Page [BibTex]

Web link (url) DOI Project Page [BibTex]


Patches, Planes and Probabilities: A Non-local Prior for Volumetric {3D} Reconstruction
Patches, Planes and Probabilities: A Non-local Prior for Volumetric 3D Reconstruction

Ulusoy, A. O., Black, M. J., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)

Abstract
In this paper, we propose a non-local structured prior for volumetric multi-view 3D reconstruction. Towards this goal, we present a novel Markov random field model based on ray potentials in which assumptions about large 3D surface patches such as planarity or Manhattan world constraints can be efficiently encoded as probabilistic priors. We further derive an inference algorithm that reasons jointly about voxels, pixels and image segments, and estimates marginal distributions of appearance, occupancy, depth, normals and planarity. Key to tractable inference is a novel hybrid representation that spans both voxel and pixel space and that integrates non-local information from 2D image segmentations in a principled way. We compare our non-local prior to commonly employed local smoothness assumptions and a variety of state-of-the-art volumetric reconstruction baselines on challenging outdoor scenes with textureless and reflective surfaces. Our experiments indicate that regularizing over larger distances has the potential to resolve ambiguities where local regularizers fail.

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YouTube pdf poster suppmat Project Page [BibTex]

YouTube pdf poster suppmat Project Page [BibTex]


Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer

Xie, J., Kiefel, M., Sun, M., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)

Abstract
Semantic annotations are vital for training models for object recognition, semantic segmentation or scene understanding. Unfortunately, pixelwise annotation of images at very large scale is labor-intensive and only little labeled data is available, particularly at instance level and for street scenes. In this paper, we propose to tackle this problem by lifting the semantic instance labeling task from 2D into 3D. Given reconstructions from stereo or laser data, we annotate static 3D scene elements with rough bounding primitives and develop a probabilistic model which transfers this information into the image domain. We leverage our method to obtain 2D labels for a novel suburban video dataset which we have collected, resulting in 400k semantic and instance image annotations. A comparison of our method to state-of-the-art label transfer baselines reveals that 3D information enables more efficient annotation while at the same time resulting in improved accuracy and time-coherent labels.

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pdf suppmat Project Page Project Page [BibTex]

pdf suppmat Project Page Project Page [BibTex]


Robot Arm Pose Estimation by Pixel-wise Regression of Joint Angles
Robot Arm Pose Estimation by Pixel-wise Regression of Joint Angles

Widmaier, F., Kappler, D., Schaal, S., Bohg, J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
To achieve accurate vision-based control with a robotic arm, a good hand-eye coordination is required. However, knowing the current configuration of the arm can be very difficult due to noisy readings from joint encoders or an inaccurate hand-eye calibration. We propose an approach for robot arm pose estimation that uses depth images of the arm as input to directly estimate angular joint positions. This is a frame-by-frame method which does not rely on good initialisation of the solution from the previous frames or knowledge from the joint encoders. For estimation, we employ a random regression forest which is trained on synthetically generated data. We compare different training objectives of the forest and also analyse the influence of prior segmentation of the arms on accuracy. We show that this approach improves previous work both in terms of computational complexity and accuracy. Despite being trained on synthetic data only, we demonstrate that the estimation also works on real depth images.

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


Optimizing for what matters: the Top Grasp Hypothesis
Optimizing for what matters: the Top Grasp Hypothesis

Kappler, D., Schaal, S., Bohg, J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
In this paper, we consider the problem of robotic grasping of objects when only partial and noisy sensor data of the environment is available. We are specifically interested in the problem of reliably selecting the best hypothesis from a whole set. This is commonly the case when trying to grasp an object for which we can only observe a partial point cloud from one viewpoint through noisy sensors. There will be many possible ways to successfully grasp this object, and even more which will fail. We propose a supervised learning method that is trained with a ranking loss. This explicitly encourages that the top-ranked training grasp in a hypothesis set is also positively labeled. We show how we adapt the standard ranking loss to work with data that has binary labels and explain the benefits of this formulation. Additionally, we show how we can efficiently optimize this loss with stochastic gradient descent. In quantitative experiments, we show that we can outperform previous models by a large margin.

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


Exemplar-based Prediction of Object Properties from Local Shape Similarity
Exemplar-based Prediction of Object Properties from Local Shape Similarity

Bohg, J., Kappler, D., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
We propose a novel method that enables a robot to identify a graspable object part of an unknown object given only noisy and partial information that is obtained from an RGB-D camera. Our method combines the benefits of local with the advantages of global methods. It learns a classifier that takes a local shape representation as input and outputs the probability that a grasp applied at this location will be successful. Given a query data point that is classified in this way, we can retrieve all the locally similar training data points and use them to predict latent global object shape. This information may help to further prune positively labeled grasp hypotheses based on, e.g. relation to the predicted average global shape or suitability for a specific task. This prediction can also guide scene exploration to prune object shape hypotheses. To learn the function that maps local shape to grasp stability we use a Random Forest Classifier. We show that our method reaches the same classification performance as the current state-of-the-art on this dataset which uses a Convolutional Neural Network. Additionally, we exploit the natural ability of the Random Forest to cluster similar data. For a positively predicted query data point, we retrieve all the locally similar training data points that are associated with the same leaf nodes of the Random Forest. The main insight from this work is that local object shape that affords a grasp is also a good predictor of global object shape. We empirically support this claim with quantitative experiments. Additionally, we demonstrate the predictive capability of the method on some real data examples.

am

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


Automatic LQR Tuning Based on Gaussian Process Global Optimization
Automatic LQR Tuning Based on Gaussian Process Global Optimization

Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 270-277, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree- of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of a two- and four- dimensional tuning problems highlight the method’s potential for automatic controller tuning on robotic platforms.

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Video - Automatic LQR Tuning Based on Gaussian Process Global Optimization - ICRA 2016 Video - Automatic Controller Tuning on a Two-legged Robot PDF DOI Project Page [BibTex]

Video - Automatic LQR Tuning Based on Gaussian Process Global Optimization - ICRA 2016 Video - Automatic Controller Tuning on a Two-legged Robot PDF DOI Project Page [BibTex]


Depth-based Object Tracking Using a Robust Gaussian Filter
Depth-based Object Tracking Using a Robust Gaussian Filter

Issac, J., Wüthrich, M., Garcia Cifuentes, C., Bohg, J., Trimpe, S., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
We consider the problem of model-based 3D- tracking of objects given dense depth images as input. Two difficulties preclude the application of a standard Gaussian filter to this problem. First of all, depth sensors are characterized by fat-tailed measurement noise. To address this issue, we show how a recently published robustification method for Gaussian filters can be applied to the problem at hand. Thereby, we avoid using heuristic outlier detection methods that simply reject measurements if they do not match the model. Secondly, the computational cost of the standard Gaussian filter is prohibitive due to the high-dimensional measurement, i.e. the depth image. To address this problem, we propose an approximation to reduce the computational complexity of the filter. In quantitative experiments on real data we show how our method clearly outperforms the standard Gaussian filter. Furthermore, we compare its performance to a particle-filter-based tracking method, and observe comparable computational efficiency and improved accuracy and smoothness of the estimates.

am ics

Video Bayesian Object Tracking Library Bayesian Filtering Framework Object Tracking Dataset link (url) DOI Project Page [BibTex]

Video Bayesian Object Tracking Library Bayesian Filtering Framework Object Tracking Dataset link (url) DOI Project Page [BibTex]


Sperm-shaped magnetic microrobots: Fabrication using electrospinning, modeling, and characterization
Sperm-shaped magnetic microrobots: Fabrication using electrospinning, modeling, and characterization

Khalil, I. S., Tabak, A. F., Hosney, A., Mohamed, A., Klingner, A., Ghoneima, M., Sitti, M.

In Robotics and Automation (ICRA), 2016 IEEE International Conference on, pages: 1939-1944, May 2016 (inproceedings)

Abstract
We use electrospinning to fabricate sperm-shaped magnetic microrobots with a range of diameters from 50 μm to 500 μm. The variables of the electrospinning operation (voltage, concentration of the solution, dynamic viscosity, and distance between the syringe needle and collector) to achieve beading effect are determined. This beading effect allows us to fabricate microrobots with similar morphology to that of sperm cells. The bead and the ultra-fine fiber resemble the morphology of the head and tail of the sperm cell, respectively. We incorporate iron oxide nanoparticles to the head of the sperm-shaped microrobot to provide a magnetic dipole moment. This dipole enables directional control under the influence of external magnetic fields. We also apply weak (less than 2 mT) oscillating magnetic fields to exert a magnetic torque on the magnetic head, and generate planar flagellar waves and flagellated swim. The average speed of the sperm-shaped microrobot is calculated to be 0.5 body lengths per second and 1 body lengths per second at frequencies of 5 Hz and 10 Hz, respectively. We also develop a model of the microrobot using elastohydrodynamics approach and Timoshenko-Rayleigh beam theory, and find good agreement with the experimental results.

pi

DOI [BibTex]

DOI [BibTex]


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Communication Rate Analysis for Event-based State Estimation

(Best student paper finalist)

Ebner, S., Trimpe, S.

In Proceedings of the 13th International Workshop on Discrete Event Systems, May 2016 (inproceedings)

am ics

PDF DOI [BibTex]

PDF DOI [BibTex]


A Lightweight Robotic Arm with Pneumatic Muscles for Robot Learning
A Lightweight Robotic Arm with Pneumatic Muscles for Robot Learning

Büchler, D., Ott, H., Peters, J.

Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 4086-4092, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (conference)

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ICRA16final DOI Project Page [BibTex]

ICRA16final DOI Project Page [BibTex]


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Drifting Gaussian Processes with Varying Neighborhood Sizes for Online Model Learning

Meier, F., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

am

[BibTex]

[BibTex]


Deep Discrete Flow
Deep Discrete Flow

Güney, F., Geiger, A.

Asian Conference on Computer Vision (ACCV), 2016 (conference) Accepted

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pdf suppmat Project Page [BibTex]

pdf suppmat Project Page [BibTex]


Ensuring Ethical Behavior from Autonomous Systems
Ensuring Ethical Behavior from Autonomous Systems

Anderson, M., Anderson, S. L., Berenz, V.

In Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Papers from the 2016 AAAI Workshop, Phoenix, Arizona, USA, February 12, 2016, 2016 (inproceedings)

Abstract
We propose a method which generates reactive robot behavior learned from human demonstration. In order to do so, we use the Playful programming language which is based on the reactive programming paradigm. This allows us to represent the learned behavior as a set of associations between sensor and motor primitives in a human readable script. Distinguishing between sensor and motor primitives introduces a supplementary level of granularity and more importantly enforces feedback, increasing adaptability and robustness. As the experimental section shows, useful behaviors may be learned from a single demonstration covering a very limited portion of the task space.

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link (url) [BibTex]

link (url) [BibTex]


no image
Towards Robust Online Inverse Dynamics Learning

Meier, F., Kappler, D., Ratliff, N., Schaal, S.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, 2016 (conference) Accepted

am

fmeier_iros_2016 [BibTex]

fmeier_iros_2016 [BibTex]


Self-Supervised Regrasping using Spatio-Temporal Tactile Features and Reinforcement Learning
Self-Supervised Regrasping using Spatio-Temporal Tactile Features and Reinforcement Learning

Chebotar, Y., Hausman, K., Su, Z., Sukhatme, G., Schaal, S.

In International Conference on Intelligent Robots and Systems (IROS) 2016, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016 (inproceedings)

am

pdf video [BibTex]

pdf video [BibTex]


Modeling Variability of Musculoskeletal Systems with Heteroscedastic Gaussian Processes
Modeling Variability of Musculoskeletal Systems with Heteroscedastic Gaussian Processes

Büchler, D., Calandra, R., Peters, J.

Workshop on Neurorobotics, Neural Information Processing Systems (NIPS), 2016 (conference)

am ei

NIPS16Neurorobotics [BibTex]

NIPS16Neurorobotics [BibTex]


Generalizing Regrasping with Supervised Policy Learning
Generalizing Regrasping with Supervised Policy Learning

Chebotar, Y., Hausman, K., Kroemer, O., Sukhatme, G., Schaal, S.

In International Symposium on Experimental Robotics (ISER) 2016, International Symposium on Experimental Robotics, 2016 (inproceedings)

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pdf video [BibTex]

pdf video [BibTex]


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On the Effects of Measurement Uncertainty in Optimal Control of Contact Interactions

Ponton, B., Schaal, S., Righetti, L.

In The 12th International Workshop on the Algorithmic Foundations of Robotics WAFR, Berkeley, USA, 2016 (inproceedings)

Abstract
Stochastic Optimal Control (SOC) typically considers noise only in the process model, i.e. unknown disturbances. However, in many robotic applications involving interaction with the environment, such as locomotion and manipulation, uncertainty also comes from lack of precise knowledge of the world, which is not an actual disturbance. We analyze the effects of also considering noise in the measurement model, by devel- oping a SOC algorithm based on risk-sensitive control, that includes the dynamics of an observer in such a way that the control law explicitly de- pends on the current measurement uncertainty. In simulation results on a simple 2D manipulator, we have observed that measurement uncertainty leads to low impedance behaviors, a result in contrast with the effects of process noise that creates stiff behaviors. This suggests that taking into account measurement uncertainty could be a potentially very interesting way to approach problems involving uncertain contact interactions.

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link (url) [BibTex]

link (url) [BibTex]