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2015


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Gaussian Process Optimization for Self-Tuning Control

Marco, A.

Polytechnic University of Catalonia (BarcelonaTech), October 2015 (mastersthesis)

am ics

PDF Project Page [BibTex]

2015


PDF Project Page [BibTex]


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Adaptive and Learning Concepts in Hydraulic Force Control

Doerr, A.

University of Stuttgart, September 2015 (mastersthesis)

am ics

[BibTex]

[BibTex]


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Object Detection Using Deep Learning - Learning where to search using visual attention

Kloss, A.

Eberhard Karls Universität Tübingen, May 2015 (mastersthesis)

Abstract
Detecting and identifying the different objects in an image fast and reliably is an important skill for interacting with one’s environment. The main problem is that in theory, all parts of an image have to be searched for objects on many different scales to make sure that no object instance is missed. It however takes considerable time and effort to actually classify the content of a given image region and both time and computational capacities that an agent can spend on classification are limited. Humans use a process called visual attention to quickly decide which locations of an image need to be processed in detail and which can be ignored. This allows us to deal with the huge amount of visual information and to employ the capacities of our visual system efficiently. For computer vision, researchers have to deal with exactly the same problems, so learning from the behaviour of humans provides a promising way to improve existing algorithms. In the presented master’s thesis, a model is trained with eye tracking data recorded from 15 participants that were asked to search images for objects from three different categories. It uses a deep convolutional neural network to extract features from the input image that are then combined to form a saliency map. This map provides information about which image regions are interesting when searching for the given target object and can thus be used to reduce the parts of the image that have to be processed in detail. The method is based on a recent publication of Kümmerer et al., but in contrast to the original method that computes general, task independent saliency, the presented model is supposed to respond differently when searching for different target categories.

am

PDF Project Page [BibTex]


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Robot Arm Tracking with Random Decision Forests

Widmaier, F.

Eberhard-Karls-Universität Tübingen, May 2015 (mastersthesis)

Abstract
For grasping and manipulation with robot arms, knowing the current pose of the arm is crucial for successful controlling its motion. Often, pose estimations can be acquired from encoders inside the arm, but they can have significant inaccuracy which makes the use of additional techniques necessary. In this master thesis, a novel approach of robot arm pose estimation is presented, that works on single depth images without the need of prior foreground segmentation or other preprocessing steps. A random regression forest is used, which is trained only on synthetically generated data. The approach improves former work by Bohg et al. by considerably reducing the computational effort both at training and test time. The forest in the new method directly estimates the desired joint angles while in the former approach, the forest casts 3D position votes for the joints, which then have to be clustered and fed into an iterative inverse kinematic process to finally get the joint angles. To improve the estimation accuracy, the standard training objective of the forest training is replaced by a specialized function that makes use of a model-dependent distance metric, called DISP. Experimental results show that the specialized objective indeed improves pose estimation and it is shown that the method, despite of being trained on synthetic data only, is able to provide reasonable estimations for real data at test time.

am

PDF Project Page [BibTex]

PDF Project Page [BibTex]


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Policy Search for Imitation Learning

Doerr, A.

University of Stuttgart, January 2015 (thesis)

am ics

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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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]


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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]


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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]


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Exciting Engineered Passive Dynamics in a Bipedal Robot

Renjewski, D., Spröwitz, A., Peekema, A., Jones, M., Hurst, J.

{IEEE Transactions on Robotics and Automation}, 31(5):1244-1251, IEEE, New York, NY, 2015 (article)

Abstract
A common approach in designing legged robots is to build fully actuated machines and control the machine dynamics entirely in soft- ware, carefully avoiding impacts and expending a lot of energy. However, these machines are outperformed by their human and animal counterparts. Animals achieve their impressive agility, efficiency, and robustness through a close integration of passive dynamics, implemented through mechanical components, and neural control. Robots can benefit from this same integrated approach, but a strong theoretical framework is required to design the passive dynamics of a machine and exploit them for control. For this framework, we use a bipedal spring–mass model, which has been shown to approximate the dynamics of human locomotion. This paper reports the first implementation of spring–mass walking on a bipedal robot. We present the use of template dynamics as a control objective exploiting the engineered passive spring–mass dynamics of the ATRIAS robot. The results highlight the benefits of combining passive dynamics with dynamics-based control and open up a library of spring–mass model-based control strategies for dynamic gait control of robots.

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]

2009


Valero-Cuevas, F., Hoffmann, H., Kurse, M. U., Kutch, J. J., Theodorou, E. A.

IEEE Reviews in Biomedical Engineering – (All authors have equally contributed), (2):110?135, 2009, clmc (article)

Abstract
Computational models of the neuromuscular system hold the potential to allow us to reach a deeper understanding of neuromuscular function and clinical rehabilitation by complementing experimentation. By serving as a means to distill and explore specific hypotheses, computational models emerge from prior experimental data and motivate future experimental work. Here we review computational tools used to understand neuromuscular function including musculoskeletal modeling, machine learning, control theory, and statistical model analysis. We conclude that these tools, when used in combination, have the potential to further our understanding of neuromuscular function by serving as a rigorous means to test scientific hypotheses in ways that complement and leverage experimental data.

am

link (url) [BibTex]

2009


link (url) [BibTex]


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Bayesian Methods for Autonomous Learning Systems (Phd Thesis)

Ting, J.

Department of Computer Science, University of Southern California, Los Angeles, CA, 2009, clmc (phdthesis)

am

PDF [BibTex]

PDF [BibTex]


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On-line learning and modulation of periodic movements with nonlinear dynamical systems

Gams, A., Ijspeert, A., Schaal, S., Lenarčič, J.

Autonomous Robots, 27(1):3-23, 2009, clmc (article)

Abstract
Abstract  The paper presents a two-layered system for (1) learning and encoding a periodic signal without any knowledge on its frequency and waveform, and (2) modulating the learned periodic trajectory in response to external events. The system is used to learn periodic tasks on a humanoid HOAP-2 robot. The first layer of the system is a dynamical system responsible for extracting the fundamental frequency of the input signal, based on adaptive frequency oscillators. The second layer is a dynamical system responsible for learning of the waveform based on a built-in learning algorithm. By combining the two dynamical systems into one system we can rapidly teach new trajectories to robots without any knowledge of the frequency of the demonstration signal. The system extracts and learns only one period of the demonstration signal. Furthermore, the trajectories are robust to perturbations and can be modulated to cope with a dynamic environment. The system is computationally inexpensive, works on-line for any periodic signal, requires no additional signal processing to determine the frequency of the input signal and can be applied in parallel to multiple dimensions. Additionally, it can adapt to changes in frequency and shape, e.g. to non-stationary signals, such as hand-generated signals and human demonstrations.

am

link (url) [BibTex]

link (url) [BibTex]


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Local dimensionality reduction for non-parametric regression

Hoffman, H., Schaal, S., Vijayakumar, S.

Neural Processing Letters, 2009, clmc (article)

Abstract
Locally-weighted regression is a computationally-efficient technique for non-linear regression. However, for high-dimensional data, this technique becomes numerically brittle and computationally too expensive if many local models need to be maintained simultaneously. Thus, local linear dimensionality reduction combined with locally-weighted regression seems to be a promising solution. In this context, we review linear dimensionality-reduction methods, compare their performance on nonparametric locally-linear regression, and discuss their ability to extend to incremental learning. The considered methods belong to the following three groups: (1) reducing dimensionality only on the input data, (2) modeling the joint input-output data distribution, and (3) optimizing the correlation between projection directions and output data. Group 1 contains principal component regression (PCR); group 2 contains principal component analysis (PCA) in joint input and output space, factor analysis, and probabilistic PCA; and group 3 contains reduced rank regression (RRR) and partial least squares (PLS) regression. Among the tested methods, only group 3 managed to achieve robust performance even for a non-optimal number of components (factors or projection directions). In contrast, group 1 and 2 failed for fewer components since these methods rely on the correct estimate of the true intrinsic dimensionality. In group 3, PLS is the only method for which a computationally-efficient incremental implementation exists. Thus, PLS appears to be ideally suited as a building block for a locally-weighted regressor in which projection directions are incrementally added on the fly.

am

link (url) [BibTex]

link (url) [BibTex]


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Incorporating Muscle Activation-Contraction dynamics to an optimal control framework for finger movements

Theodorou, Evangelos A., Valero-Cuevas, Francisco J.

Abstracts of Neural Control of Movement Conference (NCM 2009), 2009, clmc (article)

Abstract
Recent experimental and theoretical work [1] investigated the neural control of contact transition between motion and force during tapping with the index finger as a nonlinear optimization problem. Such transitions from motion to well-directed contact force are a fundamental part of dexterous manipulation. There are 3 alternative hypotheses of how this transition could be accomplished by the nervous system as a function of changes in direction and magnitude of the torque vector controlling the finger. These hypotheses are 1) an initial change in direction with a subsequent change in magnitude of the torque vector; 2) an initial change in magnitude with a subsequent directional change of the torque vector; and 3) a simultaneous and proportionally equal change of both direction and magnitude of the torque vector. Experimental work in [2] shows that the nervous system selects the first strategy, and in [1] we suggest that this may in fact be the optimal strategy. In [4] the framework of Iterative Linear Quadratic Optimal Regulator (ILQR) was extended to incorporate motion and force control. However, our prior simulation work assumed direct and instantaneous control of joint torques, which ignores the known delays and filtering properties of skeletal muscle. In this study, we implement an ILQR controller for a more biologically plausible biomechanical model of the index finger than [4], and add activation-contraction dynamics to the system to simulate muscle function. The planar biomechanical model includes the kinematics of the 3 joints while the applied torques are driven by activation?contraction dynamics with biologically plausible time constants [3]. In agreement with our experimental work [2], the task is to, within 500 ms, move the finger from a given resting configuration to target configuration with a desired terminal velocity. ILQR does not only stabilize the finger dynamics according to the objective function, but it also generates smooth joint space trajectories with minimal tuning and without an a-priori initial control policy (which is difficult to find for highly dimensional biomechanical systems). Furthemore, the use of this optimal control framework and the addition of activation-contraction dynamics considers the full nonlinear dynamics of the index finger and produces a sequence of postures which are compatible with experimental motion data [2]. These simulations combined with prior experimental results suggest that optimal control is a strong candidate for the generation of finger movements prior to abrupt motion-to-force transitions. This work is funded in part by grants NIH R01 0505520 and NSF EFRI-0836042 to Dr. Francisco J. Valero- Cuevas 1 Venkadesan M, Valero-Cuevas FJ. 
Effects of neuromuscular lags on controlling contact transitions. 
Philosophical Transactions of the Royal Society A: 2008. 2 Venkadesan M, Valero-Cuevas FJ. 
Neural Control of Motion-to-Force Transitions with the Fingertip. 
J. Neurosci., Feb 2008; 28: 1366 - 1373; 3 Zajac. Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit Rev Biomed Eng, 17 4. Weiwei Li., Francisco Valero Cuevas: ?Linear Quadratic Optimal Control of Contact Transition with Fingertip ? ACC 2009

am

PDF [BibTex]

PDF [BibTex]


no image
On-line learning and modulation of periodic movements with nonlinear dynamical systems

Gams, A., Ijspeert, A., Schaal, S., Lenarčič, J.

Autonomous Robots, 27(1):3-23, 2009, clmc (article)

Abstract
Abstract  The paper presents a two-layered system for (1) learning and encoding a periodic signal without any knowledge on its frequency and waveform, and (2) modulating the learned periodic trajectory in response to external events. The system is used to learn periodic tasks on a humanoid HOAP-2 robot. The first layer of the system is a dynamical system responsible for extracting the fundamental frequency of the input signal, based on adaptive frequency oscillators. The second layer is a dynamical system responsible for learning of the waveform based on a built-in learning algorithm. By combining the two dynamical systems into one system we can rapidly teach new trajectories to robots without any knowledge of the frequency of the demonstration signal. The system extracts and learns only one period of the demonstration signal. Furthermore, the trajectories are robust to perturbations and can be modulated to cope with a dynamic environment. The system is computationally inexpensive, works on-line for any periodic signal, requires no additional signal processing to determine the frequency of the input signal and can be applied in parallel to multiple dimensions. Additionally, it can adapt to changes in frequency and shape, e.g. to non-stationary signals, such as hand-generated signals and human demonstrations.

am

link (url) [BibTex]

link (url) [BibTex]

1994


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Robot juggling: An implementation of memory-based learning

Schaal, S., Atkeson, C. G.

Control Systems Magazine, 14(1):57-71, 1994, clmc (article)

Abstract
This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memory-based local modeling approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given to examine the uncertainty of a model, to optimize its prediction quality, and to deal with noisy and corrupted data. We develop an exploration algorithm that explicitly deals with prediction accuracy requirements during exploration. Using all these ingredients in combination with methods from optimal control, our robot achieves fast real-time learning of the task within 40 to 100 trials.

am

link (url) [BibTex]

1994


link (url) [BibTex]

1992


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Ins CAD integrierte Kostenkalkulation (CAD-Integrated Cost Calculation)

Ehrlenspiel, K., Schaal, S.

Konstruktion 44, 12, pages: 407-414, 1992, clmc (article)

am

[BibTex]

1992


[BibTex]