Header logo is


2019


Thumb xl screenshot 2019 04 08 at 16.22.00
Effect of Remote Masking on Detection of Electrovibration

Jamalzadeh, M., Güçlü, B., Vardar, Y., Basdogan, C.

In Proceedings of the IEEE World Haptics Conference (WHC), Tokyo, Japan, July 2019 (inproceedings) Accepted

Abstract
Masking has been used to study human perception of tactile stimuli, including those created on haptic touch screens. Earlier studies have investigated the effect of in-site masking on tactile perception of electrovibration. In this study, we investigated whether it is possible to change detection threshold of electrovibration at fingertip of index finger via remote masking, i.e. by applying a (mechanical) vibrotactile stimulus on the proximal phalanx of the same finger. The masking stimuli were generated by a voice coil (Haptuator). For eight participants, we first measured the detection thresholds for electrovibration at the fingertip and for vibrotactile stimuli at the proximal phalanx. Then, the vibrations on the skin were measured at four different locations on the index finger of subjects to investigate how the mechanical masking stimulus propagated as the masking level was varied. Finally, electrovibration thresholds measured in the presence of vibrotactile masking stimuli. Our results show that vibrotactile masking stimuli generated sub-threshold vibrations around fingertip, and hence did not mechanically interfere with the electrovibration stimulus. However, there was a clear psychophysical masking effect due to central neural processes. Electrovibration absolute threshold increased approximately 0.19 dB for each dB increase in the masking level.

hi

[BibTex]

2019


[BibTex]


Thumb xl screenshot 2019 04 08 at 16.08.19
Fingertip Interaction Metrics Correlate with Visual and Haptic Perception of Real Surfaces

Vardar, Y., Wallraven, C., Kuchenbecker, K. J.

In Proceedings of the IEEE World Haptics Conference (WHC), Tokyo, Japan, July 2019 (inproceedings) Accepted

Abstract
Both vision and touch contribute to the perception of real surfaces. Although there have been many studies on the individual contributions of each sense, it is still unclear how each modality’s information is processed and integrated. To fill this gap, we investigated the similarity of visual and haptic perceptual spaces, as well as how well they each correlate with fingertip interaction metrics. Twenty participants interacted with ten different surfaces from the Penn Haptic Texture Toolkit by either looking at or touching them and judged their similarity in pairs. By analyzing the resulting similarity ratings using multi-dimensional scaling (MDS), we found that surfaces are similarly organized within the three-dimensional perceptual spaces of both modalities. Also, between-participant correlations were significantly higher in the haptic condition. In a separate experiment, we obtained the contact forces and accelerations acting on one finger interacting with each surface in a controlled way. We analyzed the collected fingertip interaction data in both the time and frequency domains. Our results suggest that the three perceptual dimensions for each modality can be represented by roughness/smoothness, hardness/softness, and friction, and that these dimensions can be estimated by surface vibration power, tap spectral centroid, and kinetic friction coefficient, respectively.

hi

Project Page [BibTex]

Project Page [BibTex]


Thumb xl teaser awesome v2
Haptipedia: Accelerating Haptic Device Discovery to Support Interaction & Engineering Design

Seifi, H., Fazlollahi, F., Oppermann, M., Sastrillo, J. A., Ip, J., Agrawal, A., Park, G., Kuchenbecker, K. J., MacLean, K. E.

In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), Glasgow, Scotland, May 2019 (inproceedings) Accepted

Abstract
Creating haptic experiences often entails inventing, modifying, or selecting specialized hardware. However, experience designers are rarely engineers, and 30 years of haptic inventions are buried in a fragmented literature that describes devices mechanically rather than by potential purpose. We conceived of Haptipedia to unlock this trove of examples: Haptipedia presents a device corpus for exploration through metadata that matter to both device and experience designers. It is a taxonomy of device attributes that go beyond physical description to capture potential utility, applied to a growing database of 105 grounded force-feedback devices, and accessed through a public visualization that links utility to morphology. Haptipedia's design was driven by both systematic review of the haptic device literature and rich input from diverse haptic designers. We describe Haptipedia's reception (including hopes it will redefine device reporting standards) and our plans for its sustainability through community participation.

hi

Project Page [BibTex]

Project Page [BibTex]


no image
Internal Array Electrodes Improve the Spatial Resolution of Soft Tactile Sensors Based on Electrical Resistance Tomography

Lee, H., Park, K., Kim, J., Kuchenbecker, K. J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 2019 (inproceedings) Accepted

hi

Project Page [BibTex]

Project Page [BibTex]


Thumb xl robot
Improving Haptic Adjective Recognition with Unsupervised Feature Learning

Richardson, B. A., Kuchenbecker, K. J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 2019 (inproceedings) Accepted

Abstract
Humans can form an impression of how a new object feels simply by touching its surfaces with the densely innervated skin of the fingertips. Many haptics researchers have recently been working to endow robots with similar levels of haptic intelligence, but these efforts almost always employ hand-crafted features, which are brittle, and concrete tasks, such as object recognition. We applied unsupervised feature learning methods, specifically K-SVD and Spatio-Temporal Hierarchical Matching Pursuit (ST-HMP), to rich multi-modal haptic data from a diverse dataset. We then tested the learned features on 19 more abstract binary classification tasks that center on haptic adjectives such as smooth and squishy. The learned features proved superior to traditional hand-crafted features by a large margin, almost doubling the average F1 score across all adjectives. Additionally, particular exploratory procedures (EPs) and sensor channels were found to support perception of certain haptic adjectives, underlining the need for diverse interactions and multi-modal haptic data.

hi

Project Page [BibTex]

Project Page [BibTex]


Thumb xl learning tactile servoing thumbnail
Learning Latent Space Dynamics for Tactile Servoing

Sutanto, G., Ratliff, N., Sundaralingam, B., Chebotar, Y., Su, Z., Handa, A., Fox, D.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2019, IEEE, International Conference on Robotics and Automation, May 2019 (inproceedings) Accepted

am

pdf video [BibTex]

pdf video [BibTex]


Thumb xl screenshot 2019 02 03 at 19.15.13
A Novel Texture Rendering Approach for Electrostatic Displays

Fiedler, T., Vardar, Y.

In Proceedings of International Workshop on Haptic and Audio Interaction Design (HAID), Lille, France, March 2019 (inproceedings)

Abstract
Generating realistic texture feelings on tactile displays using data-driven methods has attracted a lot of interest in the last decade. However, the need for large data storages and transmission rates complicates the use of these methods for the future commercial displays. In this paper, we propose a new texture rendering approach which can compress the texture data signicantly for electrostatic displays. Using three sample surfaces, we first explain how to record, analyze and compress the texture data, and render them on a touchscreen. Then, through psychophysical experiments conducted with nineteen participants, we show that the textures can be reproduced by a signicantly less number of frequency components than the ones in the original signal without inducing perceptual degradation. Moreover, our results indicate that the possible degree of compression is affected by the surface properties.

hi

Fiedler19-HAID-Electrostatic [BibTex]

Fiedler19-HAID-Electrostatic [BibTex]


no image
The Perception of Ultrasonic Square Reductions of Friction With Variable Sharpness and Duration

Gueorguiev, D., Vezzoli, E., Sednaoui, T., Grisoni, L., Lemaire-Semail, B.

IEEE Transactions on Haptics, 12(2):179-188, January 2019 (article)

Abstract
The human perception of square ultrasonic modulation of the finger-surface friction was investigated during active tactile exploration by using short frictional cues of varying duration and sharpness. In a first experiment, we asked participants to discriminate the transition time and duration of short square ultrasonic reductions of friction. They proved very sensitive to discriminate millisecond differences in these two parameters with the average psychophysical thresholds being 2.3–2.4 ms for both parameters. A second experiment focused on the perception of square friction reductions with variable transition times and durations. We found that for durations of the stimulation larger than 90 ms, participants often perceived three or four edges when only two stimulations were presented while they consistently felt two edges for signals shorter than 50 ms. A subsequent analysis of the contact forces induced by these ultrasonic stimulations during slow and fast active exploration showed that two identical consecutive ultrasonic pulses can induce significantly different frictional dynamics especially during fast motion of the finger. These results confirm the human sensitivity to transient frictional cues and suggest that the human perception of square reductions of friction can depend on their sharpness and duration as well as on the speed of exploration.

hi

DOI [BibTex]

DOI [BibTex]


no image
How Does It Feel to Clap Hands with a Robot?

Fitter, N. T., Kuchenbecker, K. J.

International Journal of Social Robotics, 2019 (article) Accepted

Abstract
Future robots may need lighthearted physical interaction capabilities to connect with people in meaningful ways. To begin exploring how users perceive playful human–robot hand-to-hand interaction, we conducted a study with 20 participants. Each user played simple hand-clapping games with the Rethink Robotics Baxter Research Robot during a 1-h-long session involving 24 randomly ordered conditions that varied in facial reactivity, physical reactivity, arm stiffness, and clapping tempo. Survey data and experiment recordings demonstrate that this interaction is viable: all users successfully completed the experiment and mentioned enjoying at least one game without prompting. Hand-clapping tempo was highly salient to users, and human-like robot errors were more widely accepted than mechanical errors. Furthermore, perceptions of Baxter varied in the following statistically significant ways: facial reactivity increased the robot’s perceived pleasantness and energeticness; physical reactivity decreased pleasantness, energeticness, and dominance; higher arm stiffness increased safety and decreased dominance; and faster tempo increased energeticness and increased dominance. These findings can motivate and guide roboticists who want to design social–physical human–robot interactions.

hi

[BibTex]

[BibTex]


Thumb xl screenshot from 2019 03 21 12 11 19
Automated Generation of Reactive Programs from Human Demonstration for Orchestration of Robot Behaviors

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

ArXiv, 2019 (article)

Abstract
Social robots or collaborative robots that have to interact with people in a reactive way are difficult to program. This difficulty stems from the different skills required by the programmer: to provide an engaging user experience the behavior must include a sense of aesthetics while robustly operating in a continuously changing environment. The Playful framework allows composing such dynamic behaviors using a basic set of action and perception primitives. Within this framework, a behavior is encoded as a list of declarative statements corresponding to high-level sensory-motor couplings. To facilitate non-expert users to program such behaviors, we propose a Learning from Demonstration (LfD) technique that maps motion capture of humans directly to a Playful script. The approach proceeds by identifying the sensory-motor couplings that are active at each step using the Viterbi path in a Hidden Markov Model (HMM). Given these activation patterns, binary classifiers called evaluations are trained to associate activations to sensory data. Modularity is increased by clustering the sensory-motor couplings, leading to a hierarchical tree structure. The novelty of the proposed approach is that the learned behavior is encoded not in terms of trajectories in a task space, but as couplings between sensory information and high-level motor actions. This provides advantages in terms of behavioral generalization and reactivity displayed by the robot.

am

Support Video link (url) [BibTex]

2015


no image
Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin

Calandra, R., Ivaldi, S., Deisenroth, M., Peters, J.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 690-695, Humanoids, November 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

2015


link (url) DOI [BibTex]


no image
Evaluation of Interactive Object Recognition with Tactile Sensing

Hoelscher, J., Peters, J., Hermans, T.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 310-317, Humanoids, November 2015 (inproceedings)

am ei

DOI [BibTex]

DOI [BibTex]


no image
Optimizing Robot Striking Movement Primitives with Iterative Learning Control

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

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 80-87, Humanoids, November 2015 (inproceedings)

am ei

DOI [BibTex]

DOI [BibTex]


no image
A Comparison of Contact Distribution Representations for Learning to Predict Object Interactions

Leischnig, S., Luettgen, S., Kroemer, O., Peters, J.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 616-622, Humanoids, November 2015 (inproceedings)

am ei

DOI [BibTex]

DOI [BibTex]


no image
First-Person Tele-Operation of a Humanoid Robot

Fritsche, L., Unverzagt, F., Peters, J., Calandra, R.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 997-1002, Humanoids, November 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Probabilistic Segmentation Applied to an Assembly Task

Lioutikov, R., Neumann, G., Maeda, G., Peters, J.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 533-540, Humanoids, November 2015 (inproceedings)

am ei

DOI [BibTex]

DOI [BibTex]


Thumb xl posterior
Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results

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

Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

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. Preliminary results of a low-dimensional tuning problem highlight the method’s potential for automatic controller tuning on robotic platforms.

am ei ics pn

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


no image
Stabilizing Novel Objects by Learning to Predict Tactile Slip

Veiga, F., van Hoof, H., Peters, J., Hermans, T.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 5065-5072, IROS, September 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Model-Free Probabilistic Movement Primitives for Physical Interaction

Paraschos, A., Rueckert, E., Peters, J., Neumann, G.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 2860-2866, IROS, September 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Combined Pose-Wrench and State Machine Representation for Modeling Robotic Assembly Skills

Wahrburg, A., Zeiss, S., Matthias, B., Peters, J., Ding, H.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 852-857, IROS, September 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives

Manschitz, S., Kober, J., Gienger, M., Peters, J.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 449-455, IROS, September 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Reinforcement Learning vs Human Programming in Tetherball Robot Games

Parisi, S., Abdulsamad, H., Paraschos, A., Daniel, C., Peters, J.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 6428-6434, IROS, September 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Learning Motor Skills from Partially Observed Movements Executed at Different Speeds

Ewerton, M., Maeda, G., Peters, J., Neumann, G.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 456-463, IROS, September 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl screen shot 2015 08 22 at 21.47.37
Direct Loss Minimization Inverse Optimal Control

Doerr, A., Ratliff, N., Bohg, J., Toussaint, M., Schaal, S.

In Proceedings of Robotics: Science and Systems, Rome, Italy, Robotics: Science and Systems XI, July 2015 (inproceedings)

Abstract
Inverse Optimal Control (IOC) has strongly impacted the systems engineering process, enabling automated planner tuning through straightforward and intuitive demonstration. The most successful and established applications, though, have been in lower dimensional problems such as navigation planning where exact optimal planning or control is feasible. In higher dimensional systems, such as humanoid robots, research has made substantial progress toward generalizing the ideas to model free or locally optimal settings, but these systems are complicated to the point where demonstration itself can be difficult. Typically, real-world applications are restricted to at best noisy or even partial or incomplete demonstrations that prove cumbersome in existing frameworks. This work derives a very flexible method of IOC based on a form of Structured Prediction known as Direct Loss Minimization. The resulting algorithm is essentially Policy Search on a reward function that rewards similarity to demonstrated behavior (using Covariance Matrix Adaptation (CMA) in our experiments). Our framework blurs the distinction between IOC, other forms of Imitation Learning, and Reinforcement Learning, enabling us to derive simple, versatile, and practical algorithms that blend imitation and reinforcement signals into a unified framework. Our experiments analyze various aspects of its performance and demonstrate its efficacy on conveying preferences for motion shaping and combined reach and grasp quality optimization.

am ics

PDF Video Project Page [BibTex]

PDF Video Project Page [BibTex]


no image
LMI-Based Synthesis for Distributed Event-Based State Estimation

Muehlebach, M., Trimpe, S.

In Proceedings of the American Control Conference, July 2015 (inproceedings)

Abstract
This paper presents an LMI-based synthesis procedure for distributed event-based state estimation. Multiple agents observe and control a dynamic process by sporadically exchanging data over a broadcast network according to an event-based protocol. In previous work [1], the synthesis of event-based state estimators is based on a centralized design. In that case three different types of communication are required: event-based communication of measurements, periodic reset of all estimates to their joint average, and communication of inputs. The proposed synthesis problem eliminates the communication of inputs as well as the periodic resets (under favorable circumstances) by accounting explicitly for the distributed structure of the control system.

am ics

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


no image
Guaranteed H2 Performance in Distributed Event-Based State Estimation

Muehlebach, M., Trimpe, S.

In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

am ics

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


no image
On the Choice of the Event Trigger in Event-based Estimation

Trimpe, S., Campi, M.

In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

am ics

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


Thumb xl bottle noise
Leveraging Big Data for Grasp Planning

Kappler, D., Bohg, B., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

Abstract
We propose a new large-scale database containing grasps that are applied to a large set of objects from numerous categories. These grasps are generated in simulation and are annotated with different grasp stability metrics. We use a descriptive and efficient representation of the local object shape at which each grasp is applied. Given this data, we present a two-fold analysis: (i) We use crowdsourcing to analyze the correlation of the metrics with grasp success as predicted by humans. The results show that the metric based on physics simulation is a more consistent predictor for grasp success than the standard ε-metric. The results also support the hypothesis that human labels are not required for good ground truth grasp data. Instead the physics-metric can be used to generate datasets in simulation that may then be used to bootstrap learning in the real world. (ii) We apply a deep learning method and show that it can better leverage the large-scale database for prediction of grasp success compared to logistic regression. Furthermore, the results suggest that labels based on the physics-metric are less noisy than those from the ε-metric and therefore lead to a better classification performance.

am

PDF data DOI Project Page [BibTex]

PDF data DOI Project Page [BibTex]


no image
Event-based Estimation and Control for Remote Robot Operation with Reduced Communication

Trimpe, S., Buchli, J.

In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

Abstract
An event-based communication framework for remote operation of a robot via a bandwidth-limited network is proposed. The robot sends state and environment estimation data to the operator, and the operator transmits updated control commands or policies to the robot. Event-based communication protocols are designed to ensure that data is transmitted only when required: the robot sends new estimation data only if this yields a significant information gain at the operator, and the operator transmits an updated control policy only if this comes with a significant improvement in control performance. The developed framework is modular and can be used with any standard estimation and control algorithms. Simulation results of a robotic arm highlight its potential for an efficient use of limited communication resources, for example, in disaster response scenarios such as the DARPA Robotics Challenge.

am ics

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


Thumb xl tracking
The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems

Wüthrich, M., Bohg, J., Kappler, D., Pfreundt, C., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

Abstract
Parametric filters, such as the Extended Kalman Filter and the Unscented Kalman Filter, typically scale well with the dimensionality of the problem, but they are known to fail if the posterior state distribution cannot be closely approximated by a density of the assumed parametric form. For nonparametric filters, such as the Particle Filter, the converse holds. Such methods are able to approximate any posterior, but the computational requirements scale exponentially with the number of dimensions of the state space. In this paper, we present the Coordinate Particle Filter which alleviates this problem. We propose to compute the particle weights recursively, dimension by dimension. This allows us to explore one dimension at a time, and resample after each dimension if necessary. Experimental results on simulated as well as real data con- firm that the proposed method has a substantial performance advantage over the Particle Filter in high-dimensional systems where not all dimensions are highly correlated. We demonstrate the benefits of the proposed method for the problem of multi-object and robotic manipulator tracking.

am

arXiv Video Bayesian Filtering Framework Bayesian Object Tracking DOI Project Page [BibTex]


no image
Understanding the Geometry of Workspace Obstacles in Motion Optimization

Ratliff, N., Toussaint, M., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation, March 2015 (inproceedings)

am

PDF Video Project Page [BibTex]

PDF Video Project Page [BibTex]


no image
Learning of Non-Parametric Control Policies with High-Dimensional State Features

van Hoof, H., Peters, J., Neumann, G.

In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 995–1003, (Editors: Lebanon, G. and Vishwanathan, S.V.N. ), JMLR, AISTATS, 2015 (inproceedings)

am ei

link (url) [BibTex]

link (url) [BibTex]


no image
Data-Driven Online Decision Making for Autonomous Manipulation

Kappler, D., Pastor, P., Kalakrishnan, M., Wuthrich, M., Schaal, S.

In Proceedings of Robotics: Science and Systems, Rome, Italy, 2015 (inproceedings)

am

Project Page [BibTex]

Project Page [BibTex]


Thumb xl screenshot from 2015 09 14 11 58 36
Predicting Human Reaching Motion in Collaborative Tasks Using Inverse Optimal Control and Iterative Re-planning

Mainprice, J., Hayne, R., Berenson, D.

In Proceedings of the IEEE International Conference on Robotics and Automation, 2015 (inproceedings)

am

Project Page [BibTex]

Project Page [BibTex]


no image
Semi-Autonomous 3rd-Hand Robot

Lopes, M., Peters, J., Piater, J., Toussaint, M., Baisero, A., Busch, B., Erkent, O., Kroemer, O., Lioutikov, R., Maeda, G., Mollard, Y., Munzer, T., Shukla, D.

In Workshop on Cognitive Robotics in Future Manufacturing Scenarios, European Robotics Forum, 2015 (inproceedings)

am ei

link (url) [BibTex]

link (url) [BibTex]


no image
Active Reward Learning with a Novel Acquisition Function

Daniel, C., Kroemer, O., Viering, M., Metz, J., Peters, J.

Autonomous Robots, 39(3):389-405, 2015 (article)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Learning Inverse Dynamics Models with Contacts

Calandra, R., Ivaldi, S., Deisenroth, M., Rückert, E., Peters, J.

In IEEE International Conference on Robotics and Automation, pages: 3186-3191, ICRA, 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation

Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Peters, J.

In Proceedings of the International Symposium of Robotics Research, ISRR, 2015 (inproceedings)

am ei

link (url) [BibTex]

link (url) [BibTex]


no image
Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations

Manschitz, S., Kober, J., Gienger, M., Peters, J.

Robotics and Autonomous Systems, 74, Part A, pages: 97-107, 2015 (article)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Bayesian Optimization for Learning Gaits under Uncertainty

Calandra, R., Seyfarth, A., Peters, J., Deisenroth, M.

Annals of Mathematics and Artificial Intelligence, pages: 1-19, 2015 (article)

am ei

DOI [BibTex]

DOI [BibTex]


no image
Towards Learning Hierarchical Skills for Multi-Phase Manipulation Tasks

Kroemer, O., Daniel, C., Neumann, G., van Hoof, H., Peters, J.

In IEEE International Conference on Robotics and Automation, pages: 1503 - 1510, ICRA, 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl rsz slip objects
Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor

Su, Z., Hausman, K., Chebotar, Y., Molchanov, A., Loeb, G. E., Sukhatme, G. S., Schaal, S.

In IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 297-303, 2015 (inproceedings)

am

link (url) [BibTex]

link (url) [BibTex]


Thumb xl screen shot 2015 08 22 at 22.13.35
Policy Learning with Hypothesis Based Local Action Selection

Sankaran, B., Bohg, J., Ratliff, N., Schaal, S.

In Reinforcement Learning and Decision Making, 2015 (inproceedings)

Abstract
For robots to be able to manipulate in unknown and unstructured environments the robot should be capable of operating under partial observability of the environment. Object occlusions and unmodeled environments are some of the factors that result in partial observability. A common scenario where this is encountered is manipulation in clutter. In the case that the robot needs to locate an object of interest and manipulate it, it needs to perform a series of decluttering actions to accurately detect the object of interest. To perform such a series of actions, the robot also needs to account for the dynamics of objects in the environment and how they react to contact. This is a non trivial problem since one needs to reason not only about robot-object interactions but also object-object interactions in the presence of contact. In the example scenario of manipulation in clutter, the state vector would have to account for the pose of the object of interest and the structure of the surrounding environment. The process model would have to account for all the aforementioned robot-object, object-object interactions. The complexity of the process model grows exponentially as the number of objects in the scene increases. This is commonly the case in unstructured environments. Hence it is not reasonable to attempt to model all object-object and robot-object interactions explicitly. Under this setting we propose a hypothesis based action selection algorithm where we construct a hypothesis set of the possible poses of an object of interest given the current evidence in the scene and select actions based on our current set of hypothesis. This hypothesis set tends to represent the belief about the structure of the environment and the number of poses the object of interest can take. The agent's only stopping criterion is when the uncertainty regarding the pose of the object is fully resolved.

am

Web Project Page [BibTex]


no image
Learning Optimal Striking Points for A Ping-Pong Playing Robot

Huang, Y., Schölkopf, B., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 4587-4592, IROS, 2015 (inproceedings)

am ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Model-Based Relative Entropy Stochastic Search

Abdolmaleki, A., Peters, J., Neumann, G.

In Advances in Neural Information Processing Systems 28, pages: 3523-3531, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

am ei

link (url) [BibTex]

link (url) [BibTex]


no image
Modeling Spatio-Temporal Variability in Human-Robot Interaction with Probabilistic Movement Primitives

Ewerton, M., Neumann, G., Lioutikov, R., Ben Amor, H., Peters, J., Maeda, G.

In Workshop on Machine Learning for Social Robotics, ICRA, 2015 (inproceedings)

am ei

link (url) [BibTex]

link (url) [BibTex]


no image
Extracting Low-Dimensional Control Variables for Movement Primitives

Rueckert, E., Mundo, J., Paraschos, A., Peters, J., Neumann, G.

In IEEE International Conference on Robotics and Automation, pages: 1511-1518, ICRA, 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
A New Perspective and Extension of the Gaussian Filter

Wüthrich, M., Trimpe, S., Kappler, D., Schaal, S.

In Robotics: Science and Systems, 2015 (inproceedings)

Abstract
The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. GFs represent the belief of the current state by a Gaussian with the mean being an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependencies in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end we view the GF from a variational-inference perspective, and analyze how restrictions on the form of the belief can be relaxed while maintaining simplicity and efficiency. This analysis provides a basis for generalizations of the GF. We propose one such generalization which coincides with a GF using a virtual measurement, obtained by applying a nonlinear function to the actual measurement. Numerical experiments show that the proposed Feature Gaussian Filter (FGF) can have a substantial performance advantage over the standard GF for systems with nonlinear observation models.

am ics

Web PDF Project Page [BibTex]


no image
Learning multiple collaborative tasks with a mixture of Interaction Primitives

Ewerton, M., Neumann, G., Lioutikov, R., Ben Amor, H., Peters, J., Maeda, G.

In IEEE International Conference on Robotics and Automation, pages: 1535-1542, ICRA, 2015 (inproceedings)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Whole-body motor strategies for balancing on a beam when changing the number of available degrees of freedom

Chiovetto, E, Huber, M, Righetti, L., Schaal, S., Sternad, D, Giese, M.

In Progress in Motor Control X, Budapest, Hungry, 2015 (inproceedings)

am

[BibTex]

[BibTex]