Publications | Autonomous Motion - Max Planck Institute for Intelligent Systems

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MPI Papers

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39 results (View BibTeX file of all listed publications)

2014

Robot Arm Pose Estimation through Pixel-Wise Part Classification

Bohg, J., Romero, J., Herzog, A., Schaal, S.

In IEEE International Conference on Robotics and Automation (ICRA) 2014, pages: 3143-3150, IEEE International Conference on Robotics and Automation (ICRA), June 2014 (inproceedings)

Abstract

We propose to frame the problem of marker-less robot arm pose estimation as a pixel-wise part classification problem. As input, we use a depth image in which each pixel is classified to be either from a particular robot part or the background. The classifier is a random decision forest trained on a large number of synthetically generated and labeled depth images. From all the training samples ending up at a leaf node, a set of offsets is learned that votes for relative joint positions. Pooling these votes over all foreground pixels and subsequent clustering gives us an estimate of the true joint positions. Due to the intrinsic parallelism of pixel-wise classification, this approach can run in super real-time and is more efficient than previous ICP-like methods. We quantitatively evaluate the accuracy of this approach on synthetic data. We also demonstrate that the method produces accurate joint estimates on real data despite being purely trained on synthetic data.

video code pdf DOI Project Page [BibTex]

2014

Bohg, J., Romero, J., Herzog, A., Schaal, S. Robot Arm Pose Estimation through Pixel-Wise Part Classification In IEEE International Conference on Robotics and Automation (ICRA) 2014, pages: 3143-3150, IEEE International Conference on Robotics and Automation (ICRA), June 2014 (inproceedings)

video code pdf DOI Project Page [BibTex]

Local Gaussian Regression

Meier, F., Hennig, P., Schaal, S.

arXiv preprint, March 2014, clmc (misc)

Abstract

Abstract: Locally weighted regression was created as a nonparametric learning method that is computationally efficient, can learn from very large amounts of data and add data incrementally. An interesting feature of locally weighted regression is that it can work with ...

Web link (url) [BibTex]

Meier, F., Hennig, P., Schaal, S. Local Gaussian Regression arXiv preprint, March 2014, clmc (misc)

Web link (url) [BibTex]

A Self-Tuning LQR Approach Demonstrated on an Inverted Pendulum

Trimpe, S., Millane, A., Doessegger, S., D’Andrea, R.

In Proceedings of the 19th IFAC World Congress, Cape Town, South Africa, 2014 (inproceedings)

PDF Supplementary material DOI [BibTex]

Trimpe, S., Millane, A., Doessegger, S., D’Andrea, R. A Self-Tuning LQR Approach Demonstrated on an Inverted Pendulum In Proceedings of the 19th IFAC World Congress, Cape Town, South Africa, 2014 (inproceedings)

PDF Supplementary material DOI [BibTex]

Learning coupling terms for obstacle avoidance

Rai, A., Meier, F., Ijspeert, A., Schaal, S.

In International Conference on Humanoid Robotics, pages: 512-518, IEEE, 2014, clmc (inproceedings)

Abstract

Autonomous manipulation in dynamic environments is important for robots to perform everyday tasks. For this, a manipulator should be capable of interpreting the environment and planning an appropriate movement. At least, two possible approaches exist for this in literature. Usually, a planning system is used to generate a complex movement plan that satisfies all constraints. Alternatively, a simple plan could be chosen and modified with sensory feedback to accommodate additional constraints by equipping the controller with features that remain dormant most of the time, except when specific situations arise. Dynamic Movement Primitives (DMPs) form a robust and versatile starting point for such a controller that can be modified online using a non-linear term, called the coupling term. This can prove to be a fast and reactive way of obstacle avoidance in a human-like fashion. We propose a method to learn this coupling term from human demonstrations starting with simple features and making it more robust to avoid a larger range of obstacles. We test the ability of our coupling term to model different kinds of obstacle avoidance behaviours in humans and use this learnt coupling term to avoid obstacles in a reactive manner. This line of research aims at pushing the boundary of reactive control strategies to more complex scenarios, such that complex and usually computationally more expensive planning methods can be avoided as much as possible.

link (url) Project Page [BibTex]

Rai, A., Meier, F., Ijspeert, A., Schaal, S. Learning coupling terms for obstacle avoidance In International Conference on Humanoid Robotics, pages: 512-518, IEEE, 2014, clmc (inproceedings)

link (url) Project Page [BibTex]

Muscle Synergy Features in Behavior Adaptation and Recovery

Alnajjar, F. S., Berenz, V., Ken-ichi, O., Ohno, K., Yamada, H., Kondo, I., Shimoda, S.

In Replace, Repair, Restore, Relieve – Bridging Clinical and Engineering Solutions in Neurorehabilitation: Proceedings of the 2nd International Conference on NeuroRehabilitation (ICNR2014), Aalborg, 24-26 June, 2014, pages: 245-253, Springer International Publishing, Cham, 2014 (inbook)

link (url) DOI [BibTex]

Alnajjar, F. S., Berenz, V., Ken-ichi, O., Ohno, K., Yamada, H., Kondo, I., Shimoda, S. Muscle Synergy Features in Behavior Adaptation and Recovery In Replace, Repair, Restore, Relieve – Bridging Clinical and Engineering Solutions in Neurorehabilitation: Proceedings of the 2nd International Conference on NeuroRehabilitation (ICNR2014), Aalborg, 24-26 June, 2014, pages: 245-253, Springer International Publishing, Cham, 2014 (inbook)

link (url) DOI [BibTex]

Incremental Local Gaussian Regression

Meier, F., Hennig, P., Schaal, S.

In Advances in Neural Information Processing Systems 27, pages: 972-980, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014, clmc (inproceedings)

PDF link (url) [BibTex]

Meier, F., Hennig, P., Schaal, S. Incremental Local Gaussian Regression In Advances in Neural Information Processing Systems 27, pages: 972-980, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014, clmc (inproceedings)

PDF link (url) [BibTex]

Generalization of the tacit learning controller based on periodic tuning functions

Berenz, V., Hayashibe, M., Alnajjar, F., Shimoda, S.

In 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, pages: 893-898, 2014 (inproceedings)

DOI [BibTex]

Berenz, V., Hayashibe, M., Alnajjar, F., Shimoda, S. Generalization of the tacit learning controller based on periodic tuning functions In 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, pages: 893-898, 2014 (inproceedings)

DOI [BibTex]

Efficient Bayesian Local Model Learning for Control

Meier, F., Hennig, P., Schaal, S.

In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pages: 2244 - 2249, IROS, 2014, clmc (inproceedings)

Abstract

Model-based control is essential for compliant controland force control in many modern complex robots, like humanoidor disaster robots. Due to many unknown and hard tomodel nonlinearities, analytical models of such robots are oftenonly very rough approximations. However, modern optimizationcontrollers frequently depend on reasonably accurate models,and degrade greatly in robustness and performance if modelerrors are too large. For a long time, machine learning hasbeen expected to provide automatic empirical model synthesis,yet so far, research has only generated feasibility studies butno learning algorithms that run reliably on complex robots.In this paper, we combine two promising worlds of regressiontechniques to generate a more powerful regression learningsystem. On the one hand, locally weighted regression techniquesare computationally efficient, but hard to tune due to avariety of data dependent meta-parameters. On the other hand,Bayesian regression has rather automatic and robust methods toset learning parameters, but becomes quickly computationallyinfeasible for big and high-dimensional data sets. By reducingthe complexity of Bayesian regression in the spirit of local modellearning through variational approximations, we arrive at anovel algorithm that is computationally efficient and easy toinitialize for robust learning. Evaluations on several datasetsdemonstrate very good learning performance and the potentialfor a general regression learning tool for robotics.

PDF link (url) DOI [BibTex]

Meier, F., Hennig, P., Schaal, S. Efficient Bayesian Local Model Learning for Control In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pages: 2244 - 2249, IROS, 2014, clmc (inproceedings)

PDF link (url) DOI [BibTex]

Stability Analysis of Distributed Event-Based State Estimation

Trimpe, S.

In Proceedings of the 53rd IEEE Conference on Decision and Control, Los Angeles, CA, 2014 (inproceedings)

Abstract

An approach for distributed and event-based state estimation that was proposed in previous work [1] is analyzed and extended to practical networked systems in this paper. Multiple sensor-actuator-agents observe a dynamic process, sporadically exchange their measurements over a broadcast network according to an event-based protocol, and estimate the process state from the received data. The event-based approach was shown in [1] to mimic a centralized Luenberger observer up to guaranteed bounds, under the assumption of identical estimates on all agents. This assumption, however, is unrealistic (it is violated by a single packet drop or slight numerical inaccuracy) and removed herein. By means of a simulation example, it is shown that non-identical estimates can actually destabilize the overall system. To achieve stability, the event-based communication scheme is supplemented by periodic (but infrequent) exchange of the agentsâ?? estimates and reset to their joint average. When the local estimates are used for feedback control, the stability guarantee for the estimation problem extends to the event-based control system.

PDF Supplementary material DOI Project Page [BibTex]

Trimpe, S. Stability Analysis of Distributed Event-Based State Estimation In Proceedings of the 53rd IEEE Conference on Decision and Control, Los Angeles, CA, 2014 (inproceedings)

PDF Supplementary material DOI Project Page [BibTex]

Dual Execution of Optimized Contact Interaction Trajectories

Toussaint, M., Ratliff, N., Bohg, J., Righetti, L., Englert, P., Schaal, S.

In 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 47-54, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract

Efficient manipulation requires contact to reduce uncertainty. The manipulation literature refers to this as funneling: a methodology for increasing reliability and robustness by leveraging haptic feedback and control of environmental interaction. However, there is a fundamental gap between traditional approaches to trajectory optimization and this concept of robustness by funneling: traditional trajectory optimizers do not discover force feedback strategies. From a POMDP perspective, these behaviors could be regarded as explicit observation actions planned to sufficiently reduce uncertainty thereby enabling a task. While we are sympathetic to the full POMDP view, solving full continuous-space POMDPs in high-dimensions is hard. In this paper, we propose an alternative approach in which trajectory optimization objectives are augmented with new terms that reward uncertainty reduction through contacts, explicitly promoting funneling. This augmentation shifts the responsibility of robustness toward the actual execution of the optimized trajectories. Directly tracing trajectories through configuration space would lose all robustness-dual execution achieves robustness by devising force controllers to reproduce the temporal interaction profile encoded in the dual solution of the optimization problem. This work introduces dual execution in depth and analyze its performance through robustness experiments in both simulation and on a real-world robotic platform.

link (url) DOI [BibTex]

Toussaint, M., Ratliff, N., Bohg, J., Righetti, L., Englert, P., Schaal, S. Dual Execution of Optimized Contact Interaction Trajectories In 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 47-54, IEEE, Chicago, USA, 2014 (inproceedings)

link (url) DOI [BibTex]

Learning and Exploration in a Novel Dimensionality-Reduction Task

Ebert, J, Kim, S, Schweighofer, N., Sternad, D, Schaal, S.

In Abstracts of Neural Control of Movement Conference (NCM 2009), Amsterdam, Netherlands, 2014 (inproceedings)

[BibTex]

Ebert, J, Kim, S, Schweighofer, N., Sternad, D, Schaal, S. Learning and Exploration in a Novel Dimensionality-Reduction Task In Abstracts of Neural Control of Movement Conference (NCM 2009), Amsterdam, Netherlands, 2014 (inproceedings)

[BibTex]

Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

Herzog, A., Righetti, L., Grimminger, F., Pastor, P., Schaal, S.

In 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 981-988, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract

Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have important theoretical benefits but have never been implemented on a torque controlled robot where model inaccuracies and real-time computation requirements can be problematic. In this contribution we present an experimental evaluation of these algorithms in the context of balance control for a humanoid robot. The presented experiments demonstrate the applicability of the approach under real robot conditions (i.e. model uncertainty, estimation errors, etc). We propose a simplification of the optimization problem that allows us to decrease computation time enough to implement it in a fast torque control loop. We implement a momentum-based balance controller which shows robust performance in face of unknown disturbances, even when the robot is standing on only one foot. In a second experiment, a tracking task is evaluated to demonstrate the performance of the controller with more complicated hierarchies. Our results show that hierarchical inverse dynamics controllers can be used for feedback control of humanoid robots and that momentum-based balance control can be efficiently implemented on a real robot.

link (url) DOI [BibTex]

Herzog, A., Righetti, L., Grimminger, F., Pastor, P., Schaal, S. Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics In 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 981-988, IEEE, Chicago, USA, 2014 (inproceedings)

link (url) DOI [BibTex]

Full Dynamics LQR Control of a Humanoid Robot: An Experimental Study on Balancing and Squatting

Mason, S., Righetti, L., Schaal, S.

In 2014 IEEE-RAS International Conference on Humanoid Robots, pages: 374-379, IEEE, Madrid, Spain, 2014 (inproceedings)

Abstract

Humanoid robots operating in human environments require whole-body controllers that can offer precise tracking and well-defined disturbance rejection behavior. In this contribution, we propose an experimental evaluation of a linear quadratic regulator (LQR) using a linearization of the full robot dynamics together with the contact constraints. The advantage of the controller is that it explicitly takes into account the coupling between the different joints to create optimal feedback controllers for whole-body control. We also propose a method to explicitly regulate other tasks of interest, such as the regulation of the center of mass of the robot or its angular momentum. In order to evaluate the performance of linear optimal control designs in a real-world scenario (model uncertainty, sensor noise, imperfect state estimation, etc), we test the controllers in a variety of tracking and balancing experiments on a torque controlled humanoid (e.g. balancing, split plane balancing, squatting, pushes while squatting, and balancing on a wheeled platform). The proposed control framework shows a reliable push recovery behavior competitive with more sophisticated balance controllers, rejecting impulses up to 11.7 Ns with peak forces of 650 N, with the added advantage of great computational simplicity. Furthermore, the controller is able to track squatting trajectories up to 1 Hz without relinearization, suggesting that the linearized dynamics is sufficient for significant ranges of motion.

link (url) DOI [BibTex]

Mason, S., Righetti, L., Schaal, S. Full Dynamics LQR Control of a Humanoid Robot: An Experimental Study on Balancing and Squatting In 2014 IEEE-RAS International Conference on Humanoid Robots, pages: 374-379, IEEE, Madrid, Spain, 2014 (inproceedings)

link (url) DOI [BibTex]

State Estimation for a Humanoid Robot

Rotella, N., Bloesch, M., Righetti, L., Schaal, S.

In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 952-958, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract

This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in prior work on a point-foot quadruped platform by adding the rotational constraints imposed by the humanoid's flat feet. As in previous work, the proposed Extended Kalman Filter accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. A nonlinear observability analysis is performed on both the point-foot and flat-foot filters and it is concluded that the addition of rotational constraints significantly simplifies singular cases and improves the observability characteristics of the system. Results on a simulated walking dataset demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.

link (url) DOI [BibTex]

Rotella, N., Bloesch, M., Righetti, L., Schaal, S. State Estimation for a Humanoid Robot In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 952-958, IEEE, Chicago, USA, 2014 (inproceedings)

link (url) DOI [BibTex]

2011

Mind the gap - robotic grasping under incomplete observation

Bohg, J., Johnson-Roberson, M., Leon, B., Felip, J., Gratal, X., Bergstrom, N., Kragic, D., Morales, A.

In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages: 686-693, May 2011 (inproceedings)

Abstract

We consider the problem of grasp and manipulation planning when the state of the world is only partially observable. Specifically, we address the task of picking up unknown objects from a table top. The proposed approach to object shape prediction aims at closing the knowledge gaps in the robot's understanding of the world. A completed state estimate of the environment can then be provided to a simulator in which stable grasps and collision-free movements are planned. The proposed approach is based on the observation that many objects commonly in use in a service robotic scenario possess symmetries. We search for the optimal parameters of these symmetries given visibility constraints. Once found, the point cloud is completed and a surface mesh reconstructed. Quantitative experiments show that the predictions are valid approximations of the real object shape. By demonstrating the approach on two very different robotic platforms its generality is emphasized.

pdf video code data DOI Project Page [BibTex]

2011

Bohg, J., Johnson-Roberson, M., Leon, B., Felip, J., Gratal, X., Bergstrom, N., Kragic, D., Morales, A. Mind the gap - robotic grasping under incomplete observation In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages: 686-693, May 2011 (inproceedings)

pdf video code data DOI Project Page [BibTex]

STOMP: Stochastic trajectory optimization for motion planning

Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., Schaal, S.

In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13, 2011, clmc (inproceedings)

Abstract

We present a new approach to motion planning using a stochastic trajectory optimization framework. The approach relies on generating noisy trajectories to explore the space around an initial (possibly infeasible) trajectory, which are then combined to produced an updated trajectory with lower cost. A cost function based on a combination of obstacle and smoothness cost is optimized in each iteration. No gradient information is required for the particular optimization algorithm that we use and so general costs for which derivatives may not be available (e.g. costs corresponding to constraints and motor torques) can be included in the cost function. We demonstrate the approach both in simulation and on a dual-arm mobile manipulation system for unconstrained and constrained tasks. We experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based optimizers like CHOMP can get stuck in.

link (url) Project Page [BibTex]

Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., Schaal, S. STOMP: Stochastic trajectory optimization for motion planning In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13, 2011, clmc (inproceedings)

link (url) Project Page [BibTex]

An Experimental Demonstration of a Distributed and Event-based State Estimation Algorithm

(Best Interactive Paper Award (top out of 450))

Trimpe, S., D’Andrea, R.

In Proceedings of the 18th IFAC World Congress, 2011 (inproceedings)

PDF DOI [BibTex]

Trimpe, S., D’Andrea, R. An Experimental Demonstration of a Distributed and Event-based State Estimation Algorithm In Proceedings of the 18th IFAC World Congress, 2011 (inproceedings)

PDF DOI [BibTex]

Path Integral Control and Bounded Rationality

Braun, D. A., Ortega, P. A., Theodorou, E., Schaal, S.

In IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2011, clmc (inproceedings)

Abstract

Path integral methods [7], [15],[1] have recently been shown to be applicable to a very general class of optimal control problems. Here we examine the path integral formalism from a decision-theoretic point of view, since an optimal controller can always be regarded as an instance of a perfectly rational decision-maker that chooses its actions so as to maximize its expected utility [8]. The problem with perfect rationality is, however, that finding optimal actions is often very difficult due to prohibitive computational resource costs that are not taken into account. In contrast, a bounded rational decision-maker has only limited resources and therefore needs to strike some compromise between the desired utility and the required resource costs [14]. In particular, we suggest an information-theoretic measure of resource costs that can be derived axiomatically [11]. As a consequence we obtain a variational principle for choice probabilities that trades off maximizing a given utility criterion and avoiding resource costs that arise due to deviating from initially given default choice probabilities. The resulting bounded rational policies are in general probabilistic. We show that the solutions found by the path integral formalism are such bounded rational policies. Furthermore, we show that the same formalism generalizes to discrete control problems, leading to linearly solvable bounded rational control policies in the case of Markov systems. Importantly, Bellman?s optimality principle is not presupposed by this variational principle, but it can be derived as a limit case. This suggests that the information- theoretic formalization of bounded rationality might serve as a general principle in control design that unifies a number of recently reported approximate optimal control methods both in the continuous and discrete domain.

PDF [BibTex]

Braun, D. A., Ortega, P. A., Theodorou, E., Schaal, S. Path Integral Control and Bounded Rationality In IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2011, clmc (inproceedings)

PDF [BibTex]

Skill learning and task outcome prediction for manipulation

Pastor, P., Kalakrishnan, M., Chitta, S., Theodorou, E., Schaal, S.

In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13, 2011, clmc (inproceedings)

Abstract

Learning complex motor skills for real world tasks is a hard problem in robotic manipulation that often requires painstaking manual tuning and design by a human expert. In this work, we present a Reinforcement Learning based approach to acquiring new motor skills from demonstration. Our approach allows the robot to learn fine manipulation skills and significantly improve its success rate and skill level starting from a possibly coarse demonstration. Our approach aims to incorporate task domain knowledge, where appropriate, by working in a space consistent with the constraints of a specific task. In addition, we also present an approach to using sensor feedback to learn a predictive model of the task outcome. This allows our system to learn the proprioceptive sensor feedback needed to monitor subsequent executions of the task online and abort execution in the event of predicted failure. We illustrate our approach using two example tasks executed with the PR2 dual-arm robot: a straight and accurate pool stroke and a box flipping task using two chopsticks as tools.

link (url) Project Page Project Page [BibTex]

Pastor, P., Kalakrishnan, M., Chitta, S., Theodorou, E., Schaal, S. Skill learning and task outcome prediction for manipulation In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13, 2011, clmc (inproceedings)

link (url) Project Page Project Page [BibTex]

An Iterative Path Integral Stochastic Optimal Control Approach for Learning Robotic Tasks

Theodorou, E., Stulp, F., Buchli, J., Schaal, S.

In Proceedings of the 18th World Congress of the International Federation of Automatic Control, 2011, clmc (inproceedings)

Abstract

Recent work on path integral stochastic optimal control theory Theodorou et al. (2010a); Theodorou (2011) has shown promising results in planning and control of nonlinear systems in high dimensional state spaces. The path integral control framework relies on the transformation of the nonlinear Hamilton Jacobi Bellman (HJB) partial diﬀerential equation (PDE) into a linear PDE and the approximation of its solution via the use of the Feynman Kac lemma. In this work, we are reviewing the generalized version of path integral stochastic optimal control formalism Theodorou et al. (2010a), used for optimal control and planing of stochastic dynamical systems with state dependent control and diﬀusion matrices. Moreover we present the iterative path integral control approach, the so called Policy Improvement with Path Integrals or (PI2 ) which is capable of scaling in high dimensional robotic control problems. Furthermore we present a convergence analysis of the proposed algorithm and we apply the proposed framework to a variety of robotic tasks. Finally with the goal to perform locomotion the iterative path integral control is applied for learning nonlinear limit cycle attractors with adjustable land scape.

PDF [BibTex]

Theodorou, E., Stulp, F., Buchli, J., Schaal, S. An Iterative Path Integral Stochastic Optimal Control Approach for Learning Robotic Tasks In Proceedings of the 18th World Congress of the International Federation of Automatic Control, 2011, clmc (inproceedings)

PDF [BibTex]

Enhanced visual scene understanding through human-robot dialog

Johnson-Roberson, M., Bohg, J., Skantze, G., Gustafson, J., Carlson, R., Rasolzadeh, B., Kragic, D.

In Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pages: 3342-3348, 2011 (inproceedings)

Abstract

We propose a novel human-robot-interaction framework for robust visual scene understanding. Without any a-priori knowledge about the objects, the task of the robot is to correctly enumerate how many of them are in the scene and segment them from the background. Our approach builds on top of state-of-the-art computer vision methods, generating object hypotheses through segmentation. This process is combined with a natural dialog system, thus including a `human in the loop' where, by exploiting the natural conversation of an advanced dialog system, the robot gains knowledge about ambiguous situations. We present an entropy-based system allowing the robot to detect the poorest object hypotheses and query the user for arbitration. Based on the information obtained from the human-robot dialog, the scene segmentation can be re-seeded and thereby improved. We present experimental results on real data that show an improved segmentation performance compared to segmentation without interaction.

pdf video DOI Project Page [BibTex]

Johnson-Roberson, M., Bohg, J., Skantze, G., Gustafson, J., Carlson, R., Rasolzadeh, B., Kragic, D. Enhanced visual scene understanding through human-robot dialog In Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pages: 3342-3348, 2011 (inproceedings)

pdf video DOI Project Page [BibTex]

Risk and gain battery management for self-docking mobile robots

Berenz, V., Suzuki, K.

In Robotics and Biomimetics (ROBIO), 2011 IEEE International Conference on, pages: 1766-1771, 2011 (inproceedings)

DOI [BibTex]

Berenz, V., Suzuki, K. Risk and gain battery management for self-docking mobile robots In Robotics and Biomimetics (ROBIO), 2011 IEEE International Conference on, pages: 1766-1771, 2011 (inproceedings)

DOI [BibTex]

Reduced Communication State Estimation for Control of an Unstable Networked Control System

Trimpe, S., D’Andrea, R.

In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011 (inproceedings)

PDF Supplementary material DOI [BibTex]

Trimpe, S., D’Andrea, R. Reduced Communication State Estimation for Control of an Unstable Networked Control System In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011 (inproceedings)

PDF Supplementary material DOI [BibTex]

Neuromuscular Stochastic Optimal Control of a Tendon Driven Index Finger

Theodorou, E. A., Todorov, E., Valero-Cuevas, F.

In Proceedings of American Control Conference (ACC), 2011, clmc (inproceedings)

Abstract

With the goal to build robotic hands which can reach the levels of dexterity and robustness of the hand, the question of what are the candidate control principles that can handle the nonlinearities, the high dimensionality and the internal noise of biomechanical structures of the complexity of the hand, is still open. In this work we present the first stochastic optimal feedback controller applied to a full tendon driven simulated robotic index finger. In our model we do take into account the full tendon structure of the index finger which consist of 11 tendons based on the underlying physiology and we consider muscle with the typical force - length and force velocity properties. Our feedback controller show robustness against noise and perturbation of the dynamics while it can also successfully handle the nonlinearities and high dimensionality of the robotic index finger. Furthermore as it is shown in the evaluations, it provides the complete time history of the tendon excursions and the tendon velocities of the index finger for the tasks of tapping with zero and nonzero terminal velocities.

PDF [BibTex]

Theodorou, E. A., Todorov, E., Valero-Cuevas, F. Neuromuscular Stochastic Optimal Control of a Tendon Driven Index Finger In Proceedings of American Control Conference (ACC), 2011, clmc (inproceedings)

PDF [BibTex]