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检索条件"机构=the Learning Algorithms and Systems Laboratory"

共 81 条记录，以下是31-40 订阅

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Cognitive mechanism in synchronized motion: An internal predictive model for manual tracking control (special session)

Cognitive mechanism in synchronized motion: An internal pred...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Mahdi Khoramshahi Ashwini Shukla Aude Billard EPFL Learning Algorithms and Systems Laboratory Switzerland

Many daily tasks involve spatio-temporal coordination between two agents. Study of such coordinated actions in human-human and human-robot interaction has received increased attention of late. In this work, we use the mirror paradigm to study coupling of hand motion in a leader-follower game. The main aim of this study is to model the motion of the follower, given a particular motion of the leader. We propose a mathematical model consistent with the internal model hypothesis and the delays in the sensorimotor system. A qualitative comparison of data collected in four human dyads shows that it is possible to successfully model the motion of the follower.

关键词： Delays Lead Adaptation models Mathematical model Predictive models Trajectory Games

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learning of Grasp Adaptation through Experience and Tactile Sensing

Learning of Grasp Adaptation through Experience and Tactile ...

引用

IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Miao Li Yasemin Bekiroglu Danica Kragic Aude Billard Learning Algorithms and Systems Laboratory (LASA) at Ecole Polytechnique Federale de Lausanne (EPFL) Switzeland KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781479969357

To perform robust grasping, a multi-fingered robotic hand should be able to adapt its grasping configuration, i.e., how the object is grasped, to maintain the stability of the grasp. Such a change of grasp configuration is called grasp adaptation and it depends on the controller, the employed sensory feedback and the type of uncertainties inherit to the problem. This paper proposes a grasp adaptation strategy to deal with uncertainties about physical properties of objects, such as the object weight and the friction at the contact points. Based on an object-level impedance controller, a grasp stability estimator is first learned in the object frame. Once a grasp is predicted to be unstable by the stability estimator, a grasp adaptation strategy is triggered according to the similarity between the new grasp and the training examples. Experimental results demonstrate that our method improves the grasping performance on novel objects with different physical properties from those used for training.

关键词： Uncertainty Training Stability analysis Impedance Grasping Estimation Support vector machines

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Rewards-Driven Control of Robot Arm by Decoding EEG Signals

Rewards-Driven Control of Robot Arm by Decoding EEG Signals

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Ajay Kumar Tanwani Jose del. R. Millan Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne (EPFL) Defitech Chair on Non-Invasive Brain-Machine Interface (CNBI) Center for Neuropros-thetics in EPFL

ISBN: (纸本)9781424479276

Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not require an external cue. Our proposed system continuously estimates the goal throughout a trial starting before the movement onset by online classification and generates optimal trajectories for driving the robot arm to the estimated goal. Experiments using EEG signals of one healthy subject (right arm) yield smooth reaching movements of the simulated 7 degrees of freedom KUKA robot arm in planar center-out reaching task with approximately 80% accuracy of reaching the actual goal.

关键词： Electroencephalography Robots Decoding Trajectory Accuracy Correlation Training

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learning cost function and trajectory for robotic writing motion

Learning cost function and trajectory for robotic writing mo...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Hang Yin Ana Paiva Aude Billard Intelligent Agents and Synthetic Characters Group (GAIPS) University of Lisbon Portugal Ecole Polytechnique Fédérale de Lausanne (EPFL) Learning Algorithms and Systems Laboratory (LASA) Switzerland

ISBN: (纸本)9781479971756

We present algorithms for inferring the cost function and reference trajectory from human demonstrations of hand-writing tasks. These two key elements are then used, through optimal control, to generate an impedance-based controller for a robotic hand. The key novelty lies in the flexibility of the feature design in the composition of the cost function, in contrast to the traditional approaches that consider linearly combined features. Cross-entropy-based methods form the core of our learning technique, resulting in sample-based stochastic algorithms for task encoding and decoding. The algorithms are validated using an anthropomorphic robot hand. We assess that the correct compliance is well encapsulated by subjecting the robot to perturbations during task reproduction.

关键词： Trajectory Robots Cost function Optimal control Decoding Stochastic processes

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Stretchable capacitive tactile skin on humanoid robot fingers — First experiments and results

Stretchable capacitive tactile skin on humanoid robot finger...

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IEEE-RAS International Conference on Humanoid Robots

作者： Aaron P. Gerratt Nicolas Sommer Stéphanie P. Lacour Aude Billard Laboratory for Soft Bioelectronic Interfaces Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Learning Algorithms and Systems Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland

A stretchable tactile sensor skin has been demonstrated on the dorsal side of a robotic hand for the first time. The sensors can detect normal pressures on the same scale as human skin but also in excess of 250 kPa and withstand strains in excess of 15%. Using tactile information from the sensors mounted on a glove worn by a humanoid robot's hand, obstacle detection and surface reconstruction tasks were successfully completed in order to demonstrate the performance of the sensors under applied strains and pressure.

关键词： Strain Robot sensing systems Electrodes Skin Joints

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Towards Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion

Towards Consistent Reconstructions of Indoor Spaces Based on...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Haiwei Dong Nadia Figueroa Abdulmotaleb El Saddik Multimedia Communications Research Lab School of Electrical Engineering and Computer Science University of Ottawa Ontario Canada Learning Algorithms and Systems Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781479969357

We focus on generating consistent reconstructions of indoor spaces from a freely moving handheld RGB-D sensor, with the aim of creating virtual models that can be used for measuring and remodeling. We propose a novel 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative-Closest-Point) to fine-tune the frame-to-frame relative pose and fuse the Depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any post-processing steps.

关键词： indoor spaces odometry process step Remodeling

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Transfer in Inverse Reinforcement learning for Multiple Strategies

Transfer in Inverse Reinforcement Learning for Multiple Stra...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Ajay Kumar Tanwani Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne Switzerland

ISBN: (纸本)9781467363563

We consider the problem of incrementally learning different strategies of performing a complex sequential task from multiple demonstrations of an expert or a set of experts. While the task is the same, each expert differs in his/her way of performing it. We assume that this variety across experts' demonstration is due to the fact that each expert/strategy is driven by a different reward function, where reward function is expressed as a linear combination of a set of known features. Consequently, we can learn all the expert strategies by forming a convex set of optimal deterministic policies, from which one can match any unseen expert strategy drawn from this set. Instead of learning from scratch every optimal policy in this set, the learner transfers knowledge from the set of learned policies to bootstrap its search for new optimal policy. We demonstrate our approach on a simulated mini-golf task where the 7 degrees of freedom Barrett WAM robot arm learns to sequentially putt on different holes in accordance with the playing strategies of the expert.

关键词： Multiple Strategies WAM playing strategies

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Safety issues in human-robot interactions

Safety issues in human-robot interactions

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Milos Vasic Aude Billard Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Learning Algorithms and Systems Laboratory School of Engineering Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

Safety is an important consideration in human-robot interactions (HRI). Robots can perform powerful movements that can cause hazards to humans surrounding them. To prevent accidents, it is important to identify sources of potential harm, to determine which of the persons in the robot's vicinity may be in greatest peril and to assess the type of injuries the robot may cause to this person. This survey starts with a review of the safety issues in industrial settings, where robots manipulate dangerous tools and move with extreme rapidity and force. We then move to covering issues related to the growing numbers of autonomous mobile robots that operate in crowded (human-inhabited) environments. We discuss the potential benefits of fully autonomous cars on safety on roads and for pedestrians. Lastly, we cover safety issues related to assistive robots.

关键词： Robot sensing systems Service robots Hazards Accidents Injuries

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learning a real time grasping strategy

Learning a real time grasping strategy

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Bidan Huang Sahar El-Khoury Miao Li Joanna J. Bryson Aude Billard Learning Algorithms and Systems Laboratory (LASA) Swiss Federal Institute of Technology Lausanne (EPFL) Switzerland Computer Science Department University of Bath UK

Real time planning strategy is crucial for robots working in dynamic environments. In particular, robot grasping tasks require quick reactions in many applications such as human-robot interaction. In this paper, we propose an approach for grasp learning that enables robots to plan new grasps rapidly according to the object's position and orientation. This is achieved by taking a three-step approach. In the first step, we compute a variety of stable grasps for a given object. In the second step, we propose a strategy that learns a probability distribution of grasps based on the computed grasps. In the third step, we use the model to quickly generate grasps. We have tested the statistical method on the 9 degrees of freedom hand of the iCub humanoid robot and the 4 degrees of freedom Barrett hand. The average computation time for generating one grasp is less than 10 milliseconds. The experiments were run in Matlab on a machine with 2.8GHz processor.

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On the Influence of Emotional Feedback on Emotion Awareness and Gaze Behavior

On the Influence of Emotional Feedback on Emotion Awareness ...

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International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Fabien Ringeval Andreas Sonderegger Basilio Noris Aude Billard Juergen Sauer Denis Lalanne Document Image and Voice Analysis group University of Fribourg Fribourg Switzerland Universite de Fribourg Fribourg FR CH Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Cognitive Ergonomic group University of Fribourg Fribourg Switzerland

This paper examines how emotion feedback influences emotion awareness and gaze behavior. Simulating a videoconference setup, 36 participants watched 12 emotional video sequences that were selected from the SEMAINE database. All participants wore an eye-tracker to measure gaze behavior and were asked to rate the perceived emotion for each video sequence. 3 conditions were tested: (c1) no feedback, i.e., the original video-sequences, (c2) correct feedback, i.e., an emoticon is integrated in the video to show the emotion depicted by the person in the video and (c3) random feedback, i.e., the emoticon displays at random an emotional state that may or may not correspond to the one of the person. The results showed that emotion feedback had a significant influence on gaze behavior, e.g., over time random feedback led to a decrease in the frequency of episodes of gaze. No effect of emotion display was observed for emotion recognition. However, experiments on the automatic emotion recognition using gaze behavior provided good performance, with better score on arousal than valence, and a very good performance was obtained in the automatic recognition of the correctness of the emotion feedback.

关键词： Emotion recognition Databases Estimation Mood Cameras Educational institutions Teamwork

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