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Leader-follower:Human-centered intention-guided controller for novel SuperLimb with application to load-carrying scenarios

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Science China(Technological Sciences) 2025年第1期68卷 321-334页

作者： Qinghua ZHANG Xianglong LI Hongwei JING Kerui SUN Yuancheng LI Sikai ZHAO Lele LI Haotian JU Ziqi WANG Rui WU Tianjiao ZHENG Jie ZHAO Yanhe ZHU The State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China The Learning Algorithms and Systems Laboratory School of EngineeringÉcole Polytechnique Fédérale de LausanneLausanne CH 101510587Switzerland

Supernumerary robotic arms(SuperLimb)are a new type of wearable robot that works closely with humans as a third hand to augment human operation *** conveyance of wearers'intentions,allocation of roles,and humancentered interaction considerations are the key points in the process of human-SuperLimb *** paper proposes a human-centered intention-guided leader-follower controller that relies on the dynamic modeling of SuperLimb with application to load-carrying *** proposed leader-follower controller takes the human as the leader and the SuperLimb as the follower,achieving effective information communication,autonomous coordination,and good force compliance between SuperLimb,humans,and the environment under human safety ***,the human-SuperLimb dynamic system is modeled to achieve force interaction with the environment and ***,to achieve the precise intention extraction of humans,pose data from five visual odometry sensors are fused to capture the human state,the generalized position,the velocity of hands,and the surface electromyography signals from two myoelectric bracelets sensors are processed to recognize the natural hand gestures during load-carrying scenarios by a designed Swin transformer ***,based on the real-time distance detection between human and mechanical limbs,the security assurance and force-compliant interaction of the human-SuperLimb system are ***,the human hand muscle intention recognition,human-robot safety strategy verification,and comparative load-carrying experiments with and without the proposed method are conducted on the SuperLimb *** showed that the task parameters are well estimated to produce more reasonable planning trajectories,and SuperLimb could well understand the wearer's intentions to switch different SuperLimb *** proposed sensor-based human-robot communication framework motivates future studies of other collaboration scenes fo

关键词： SuperLimb leader-follower human intention estimation human-robot safety human-robot communication

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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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learning from failure

Learning from failure

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6th ACM/IEEE International Conference on Human-Robot Interaction, HRI 2011

作者： Grollman, Daniel H. Billard, Aude G. Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (纸本)9781450305617

In the canonical Robot learning from Demonstration scenario a robot observes performances of a task and then develops an autonomous controller. Current work acknowledges that humans may be suboptimal demonstrators and refines the controller for improved performance. However, there is still an assumption that the demonstrations are successful examples of the task. We here consider the possibility that the human has failed, and propose a model to minimize the possibility of the robot making the same mistakes.

关键词： Robots

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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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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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Statistical learning by imitation of competing constraints in joint space and task space

Statistical learning by imitation of competing constraints i...

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作者： Calinon, Sylvain Billard, Aude Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland

We present a probabilistic architecture for solving generically the problem of extracting the task constraints through a programming by demonstration framework and for generalizing the acquired knowledge to various situations. In previous work, we proposed an approach based on Gaussian mixture regression to find a controller for the robot reproducing the statistical characteristics of a movement in joint space and in task space through Lagrange optimization. In this paper, we develop an alternative procedure to handle simultaneously constraints in joint space and in task space by combining directly the probabilistic representation of the task constraints with a solution to Jacobian-based inverse kinematics. The method is validated in manipulation tasks with two 5-d.o.f. Katana robotic arms displacing a set of objects. © Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2009.

关键词： Inverse kinematics

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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...

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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

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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Donut as I do: learning from failed demonstrations

Donut as I do: Learning from failed demonstrations

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2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Daniel H Grollman Aude Billard Learning Algorithms and Systems Laboratory EPFL

The canonical Robot learning from Demonstration scenario has a robot observing human demonstrations of a task or behavior in a few situations, and then developing a generalized controller. Current work further refines the learned system, often to perform the task better than the human could. However, the underlying assumption is that the demonstrations are successful, and are appropriate to reproduce. We, instead, consider the possibility that the human has failed in their attempt, and their demonstration is an example of what not to do. Thus, instead of maximizing the similarity of generated behaviors to those of the demonstrators, we examine two methods that deliberately avoid repeating the human’s mistakes.

关键词： Trajectory Robots Humans Mathematical model Convergence Equations learning

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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...

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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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