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15 results (BibTeX)

2014


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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]

2014

DOI [BibTex]


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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]

[BibTex]


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

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

In 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2014 (inproceedings)

Project Page [BibTex]

Project Page [BibTex]


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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]

video code pdf DOI Project Page [BibTex]


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Experiments with Hierarchical Inverse Dynamics Controllers on a Torque Controlled Humanoid

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

In Proceedings of Dynamic Walking, Zürich, Switzerland, 2014, clmc (inproceedings)

Abstract
We expect autonomous legged robots to perform complex tasks in persistent interaction with an uncertain and changing environment (e.g. in a disaster relief scenario). Therefore, we need to design algorithms that can generate precise but compliant motions while optimizing the interactions with the environment. In this context, torque control algorithms often offer high performance for motion control while guaranteeing a certain level of compliance. In addition they allow for direct control of interaction forces with the environment. Recent contributions have demonstrated the relevance of torque con- trol approaches for humanoid robots, for example for balanc- ing capabilities [5, 6]. Among those we find passivity-based approaches [5] that regulate the position of the Center of Mass (CoM) by applying admissible contact forces under the quasi- static assumption. On the one hand, these approaches do not rely on a precise dynamic model of the robot while natu- rally guaranteeing robustness due to the passivity property of the controller. On the other hand the quasi-static assumption might be limiting for dynamic motions. A promising way of leveraging this limitation are control algorithms that take the full dynamic model into account [6]. However, the need for a precise dynamic model, sensor noise (particularly in the ve- locities) and limited torque bandwidth makes them more chal- lenging to implement. Moreover, it is generally required to simplify the optimization process to meet time requirements of fast control loops (typically 1 kHz on modern torque con- trolled robots). Practical evaluations of both approaches are still rare due to the lack of torque controlled humanoid plat- forms and the complexity in conducting such robot experiments.

PDF link (url) [BibTex]


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

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

Proceedings of the IEEE International Conference on Intelligent Robotics Systems, Chicago, IL, September 2014 (conference)

Abstract
Recently several hierarchical inverse dynamicscontrollers based on cascades of quadratic programs havebeen proposed for application on torque controlled robots.They have important theoretical benefits but have never beenimplemented on a torque controlled robot where model inaccuraciesand real-time computation requirements can beproblematic. In this contribution we present an experimentalevaluation of these algorithms in the context of balance controlfor a humanoid robot. The presented experiments demonstratethe applicability of the approach under real robot conditions(i.e. model uncertainty, estimation errors, etc). We propose asimplification of the optimization problem that allows us todecrease computation time enough to implement it in a fasttorque control loop. We implement a momentum-based balancecontroller which shows robust performance in face of unknowndisturbances, even when the robot is standing on only onefoot. In a second experiment, a tracking task is evaluatedto demonstrate the performance of the controller with morecomplicated hierarchies. Our results show that hierarchicalinverse dynamics controllers can be used for feedback controlof humanoid robots and that momentum-based balance controlcan be efficiently implemented on a real robot.

Video pdf DOI Project Page [BibTex]

Video pdf DOI Project Page [BibTex]


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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 Project Page [BibTex]

PDF Supplementary material DOI Project Page [BibTex]


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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]

link (url) Project Page [BibTex]


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Towards Full System Linear Quadratic Regulators for Humanoid Control

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

In Proceedings of Dynamic Walking, Zurich, Switzerland, 2014, clmc (inproceedings)

Abstract
Robots that are to locomote in a human like fashion requirecontrol of high degree of freedom (DOF) motions potentiallycoupled in a complex way. It remains challenging to expressthe task objective in an intuitive way and simultaneously generatefeedback gains guaranteeing some level of optimality.In response to this, a number of different simplified modelshave been developed to highlight different aspects of the humanoidâ??sdynamics that are important for specific tasks. Ashort list of some of the models used to represent a humanoidinclude the cart-table, double inverted pendulum, reactionmass pendulum, and automatically generated task specific reducedmodels [4]. These simplified models make planningeasier but come at the cost of modelling error and limitationson motion. In addition, one is tasked with finding mappingsbetween the full system to the reduced system. These mappingscan potentially destroy the intuition surrounding the useof the simplified model as they may not always behave as expected.By working with the full dynamics, one obtains anincrease in generality, accuracy, and eliminates the need formappings.

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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State Estimation for a Humanoid Robot

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

In Proceedings of the 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 952-958, Chicago, IL, 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 humanoidâ??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.

PDF link (url) Project Page [BibTex]

PDF link (url) Project Page [BibTex]


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State Estimation for Walking Humanoids on Unknown Terrain

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

In Proceedings of Dynamic Walking, Zürich, Switzerland, 2014, clmc (inproceedings)

Abstract
State estimation plays a crucial role in humanoid locomotion;accurate estimates of the pose and velocity of the robotâ??s baseare necessary for walking tasks. Estimation in robotics haslong been focused on mobile robot localization, where wheelodometry and exteroceptive sensor data are fused to provideestimates of absolute position and yaw. While wheeled robotsare assumed to remain stable and in contact at all times,legged locomotion inherently involves intermittent contacts.This makes stability a concern and complicates odometrybasedapproaches, distinguishing estimation for legged systemsfrom that for wheeled robots. More recent approacheson quadruped and hexapod platforms make unreasonable assumptionsabout walking gaits, assume knowledge of the terrainand use exteroceptive sensor data for corrections. However,the utility of such platforms is their potential for operationin unstructured environments in which gaits are reactive,the terrain is unknown and such sensors are unfit for use. Motivatedby the task of providing robust and generic state estimationfor humanoid robots walking on unknown terrain, weintroduce an estimation framework [1] which employs onlyproprioceptive sensors and knowledge of leg kinematics.

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Dual Execution of Optimized Contact Interaction Trajectories

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

In Proceedings of the International Conference on Intelligent Robots and Systems, Chicago, IL, October 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 obser- vation 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 robustnessâ??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.

PDF Video DOI Project Page [BibTex]

PDF Video DOI Project Page [BibTex]


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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) Project Page Project Page [BibTex]

PDF link (url) Project Page Project Page [BibTex]


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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 Project Page Project Page [BibTex]

PDF link (url) DOI Project Page Project Page [BibTex]


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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]

PDF Supplementary material DOI Project Page [BibTex]