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

共 81 条记录，以下是21-30 订阅

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An under actuated robotic arm with adjustable stiffness shape memory polymer joints

An under actuated robotic arm with adjustable stiffness shap...

引用

2015 IEEE International Conference on Robotics and Automation, ICRA 2015

作者： Firouzeh, Amir Mirrazavi Salehian, Seyed Sina Billard, Aude Paik, Jamie Reconfigurable Robotics Lab EPFL Lausanne Switzerland Learning Algorithms and Systems Laboratory EPFL Lausanne Switzerland

ISBN: (纸本)9781479969234

Various robotic applications including surgical instruments, wearable robots and autonomous mobile robots are often constrained with strict design requirements on high degrees of freedom (DoF) and minimal volume and weight. An intuitive design to meet these contradictory requirements is to embed locking mechanism in under actuated robotic manipulators to direct the actuation from a single and remote source to drive different joints on demand. Mechanical clutches do serve such purposes but often are bulky and require auxiliary mechanism making it difficult to justify the high cost adding the additional DoF, especially in cm scale. Here, we introduce an under-actuated robotic arm with shape memory polymer (SMP) joints. Through controlling the temperature, the stiffness of the joints can be adjusted and selected joints will be activated while the rest are fixed in their position. The presented prototype can control the joints independently with a coupled actuation from two stepper motors. Since we have redundant DoFs in the arm, there can be more than one configuration to reach a given position. We use a probabilistic technique to determine the optimum configuration with the minimum number of active joints that can yield the desired posture. In this paper, we report on the performance of the proposed design for the hardware and the configuration planner. © 2015 IEEE.

关键词： Robotic arms

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Metrics for Assessing Human Skill When Demonstrating a Bimanual Task to a Robot 10

Metrics for Assessing Human Skill When Demonstrating a Biman...

引用

10th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2015

作者： Pais Ureche, Ana-Lucia Billard, Aude Learning Systems and Algorithms Laboratory École Polytechnique Fédérale de Lausanne LausanneCH-1015 Switzerland

ISBN: (纸本)9781450333184

One of the major challenges in Programming by Demonstration is deciding who to imitate. In this paper we propose a set of metrics for assessing how skilled a user is when demonstrating a bimanual task to a robot, that requires both a coordinated motion of the arms, and proper contact forces. We record successful demonstrations relative to the task goal and evaluate user performance with respect to 3 measures: the ability to maneuver the tool, the consistency in teaching, and the degree of coordination between the two arms. We present preliminary results on a scooping task. © 2015 Authors.

关键词： Demonstrations

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Analyzing Human Behavior and Bootstrapping Task Constraints from Kinesthetic Demonstrations 10

Analyzing Human Behavior and Bootstrapping Task Constraints ...

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10th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2015

作者： Pais Ureche, Ana Lucia Billard, Aude Learning Systems and Algorithms Laboratory École Polytechnique Fédérale de Lausanne LausanneCH-1015 Switzerland

ISBN: (纸本)9781450333184

In robot Programming by Demonstration (PbD), the interaction with the human user is key to collecting good demonstrations, learning and finally achieving a good task execution. We therefore take a dual approach in analyzing demonstration data. First we automatically determine task constraints that can be used in the learning phase. Specifically we determine the frame of reference to be used in each part of the task, the important variables for each axis and a stiffness modulation factor. Additionally for bi-manual tasks we determine arm-dominance and spatial or force coordination patterns between the arms. Second we analyze human behavior during demonstration in order to determine how skilled the human user is and what kind of feedback is preferred during the learning interaction. We test this approach on complex tasks requiring sequences of actions, bi-manual or arm-hand coordination and contact on each end effector. © 2015 Authors.

关键词： Demonstrations

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learning Bimanual Coordinated Tasks from Human Demonstrations 10

Learning Bimanual Coordinated Tasks from Human Demonstration...

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10th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2015

作者： Pais Ureche, Ana-Lucia Billard, Aude Learning Systems and Algorithms Laboratory École Polytechnique Fédérale de Lausanne LausanneCH-1015 Switzerland

ISBN: (纸本)9781450333184

In robot programming by demonstration dealing with high dimensional data that comes from human demonstrations is often subject to embedding prior knowledge of which variables should be retained and why. This paper proposes an approach for automatizing robot learning through the detection of causalities in the set of variables recorded during demonstration. This allows us to infer a notion of coherence and coordination between multiple systems that apparently work independently. We test the approach on a bimanual scooping task, consisting of multiple phases. We detect the coordination between the two arms, between the arms and the hands and between the fingers of each hand and observe how these coordination patterns change throughout the task. © 2015 Authors.

关键词： Demonstrations

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On the Evolution of Fingertip Grasping Manifolds

On the Evolution of Fingertip Grasping Manifolds

引用

IEEE International Conference on Robotics and Automation

作者： K. Hang J. A. Haustein M. Li A. Billard C. Smith D. Kragic the Computer Vision and Active Perception Lab CAS CSC at KTH Royal Institute of Technology Stockholm Sweden the Learning Algorithms and Systems Laboratory (LASA) at école Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781467380270

Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. During execution the system plans and executes fingertip grasps using Canny's grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution. The system is evaluated both in simulation and on a Schunk-SDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system's experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.

关键词： Evolution Fingertip Grasping

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Open Robotics Research Using Web-based Knowledge Services

Open Robotics Research Using Web-based Knowledge Services

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IEEE International Conference on Robotics and Automation

作者： Michael Beetz Daniel BeBler Jan Winkler Jan-Hendrik Worch Ferenc Bálint-Benczédi Georg Bartels Aude Billard Asil Kaan Bozcuoglu Zhou Fang Nadia Figueroa Andrei Haidu Hagen Langer Alexis Maldonado Ana Lucia Pais Ureche Moritz Tenorth Thiemo Wiedemeyer the Institute for Artificial Intelligence Universit?t Bremen Am Fallturm 1 28359 Bremen Germany. the Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne CH-1015 Lausanne Switzerland the Magazino GmbH Landsberger Str. 234 80687 München Germany

ISBN: (纸本)9781467380270

In this paper we discuss how the combination of modern technologies in "big data" storage and management, knowledge representation and processing, cloud-based computation, and web technology can help the robotics community to establish and strengthen an open research discipline. We describe how we made the demonstrator of a EU project review openly available to the research community. Specifically, we recorded episodic memories with rich semantic annotations during a pizza preparation experiment in autonomous robot manipulation. Afterwards, we released them as an open knowledge base using the cloud- and web-based robot knowledge service OPENEASE. We discuss several ways on how this open data can be used to validate our experimental reports and to tackle novel challenging research problems.

关键词： how combination modern

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EMG-Based Analysis of the Upper Limb Motion 10

EMG-Based Analysis of the Upper Limb Motion

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10th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2015

作者： Batzianoulis, Iason El-Khoury, Sahar Micera, Silvestro Billard, Aude Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Translational Neural Engineering Lab Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland BioRobotics Institute Scuola Superiore sant'Anna Pisa Italy

ISBN: (纸本)9781450333184

In a human robot interaction scenario, predicting the human motion intention is essential for avoiding inconvenient delays and for a smooth reactivity of the robotic system. In particular, when dealing with hand prosthetic devices, an early estimation of the final hand gesture is crucial for a smooth control of the robotic hand. In this work we develop an electromyographic (EMG) based learning approach that decodes the grasping intention at an early stage of the reaching to grasping motion, i.e before the final grasp/hand preshape takes place. EMG electrodes are used for recording the arm muscles activities and a cyberglove is used to measure the finger joints during the textit{reach and} textit{grasp} motion. Results show that we can correctly classify with $90%$ accuracy for three typical grasps textit{before the onset of the hand pre-shape}. Such an early detection of the grasp intention allows to control a robotic hand simultaneously to the motion of subject's arm, hence generating no delay between the natural arm motion and the artificial hand motion. © 2015 Authors.

关键词： Electromyography

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EMG-based learning approach for estimating wrist motion

EMG-based learning approach for estimating wrist motion

引用

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

作者： S. El-Khoury I. Batzianoulis C. W. Antuvan S. Contu L. Masia S. Micera A. Billard Learning Algorithms and Systems Laboratory (LASA)at Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland Assistive Robotics and Interactive Ergonomic Systems Laboratory (ARIES) at Nanyang Technological University Singapore Translational Neural Engineering Laboratory (TNE) at Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781424492695

This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forearm and uses Support Vector Regression to estimate the intended displacement of the wrist. Using data recorded with the arm outstretched in various locations in space, we train the algorithm so as to allow robust prediction even when the subject moves his/her arm across several positions in space. The proposed approach was tested on five healthy subjects and showed that a R index of 63.6% is obtained for generalization across different arm positions and wrist joint angles.

关键词： EMG EMG-based learning approach estimating wrist motion

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learning compliant manipulation through kinesthetic and tactile human-robot interaction

Learning compliant manipulation through kinesthetic and tact...

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作者： Kronander, Klas Billard, Aude Learning Algorithms and Systems Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland

Robot learning from Demonstration (RLfD) has been identified as a key element for making robots useful in daily lives. A wide range of techniques has been proposed for deriving a task model from a set of demonstrations of the task. Most previous works use learning to model the kinematics of the task, and for autonomous execution the robot then relies on a stiff position controller. While many tasks can and have been learned this way, there are tasks in which controlling the position alone is insufficient to achieve the goals of the task. These are typically tasks that involve contact or require a specific response to physical perturbations. The question of how to adjust the compliance to suit the need of the task has not yet been fully treated in Robot learning from Demonstration. In this paper, we address this issue and present interfaces that allow a human teacher to indicate compliance variations by physically interacting with the robot during task execution. We validate our approach in two different experiments on the 7 DoF Barrett WAM and KUKA LWR robot manipulators. Furthermore, we conduct a user study to evaluate the usability of our approach from a non-roboticists perspective. © 2013 IEEE.

关键词： Human robot interaction

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An under actuated robotic arm with adjustable stiffness shape memory polymer joints

An under actuated robotic arm with adjustable stiffness shap...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Amir Firouzeh Seyed Sina Mirrazavi Salehian Aude Billard Jamie Paik Amir Firouzeh and Jamie paik are with Reconfigurable Robotics Lab at EPFL Lausanne Ecole Polytechnique Federale de Lausanne Lausanne VD CH Seyed Sina Mirrazavi Salehian and Aude Billard are with Learning Algorithms and Systems Laboratory at EPFL Lausanne

ISBN: (纸本)9781479969241

关键词： Joints Robots Heating Polymers Tendons Shape Temperature

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