Results 21 to 30 of about 96,046 (256)
Heterogeneous Learning from Demonstration [PDF]
2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2019 The development of human-robot systems able to leverage the strengths of both humans and their robotic counterparts has been greatly sought after because of the foreseen, broad-ranging impact across industry and research. We believe the true potential of these systems cannot be reached unless the robot is able to act with a high level of autonomy ...
Rohan R. Paleja, Matthew C. Gombolay
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Learning generalizable behaviors from demonstration
Frontiers in Neurorobotics, 2022 Generalizing prior experiences to complete new tasks is a challenging and unsolved problem in robotics. In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control (PICO).
Corban Rivera +5 more
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Motion Planning With Success Judgement Model Based on Learning From Demonstration
IEEE Access, 2020 A technique named Learning from Demonstration allows robots to learn actions in a human living environment from the demonstrations directly. In a learning method from demonstrations directly, however, teaching actions cannot be reused between situations ...
Daichi Furuta +3 more
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Assessing handwriting task difficulty levels through kinematic features: a deep-learning approach
Frontiers in Robotics and AI, 2023 Introduction: Handwriting is a complex task that requires coordination of motor, sensory, cognitive, memory, and linguistic skills to master. The extent these processes are involved depends on the complexity of the handwriting task.
Vahan Babushkin +5 more
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Imitation Learning from Purified Demonstrations
CoRR, 2023 Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning.
Wang, Yunke +4 more
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Behavior recognition for Learning from Demonstration [PDF]
2010 IEEE International Conference on Robotics and Automation, 2010 Two methods for behavior recognition are presented and evaluated. Both methods are based on the dynamic temporal difference algorithm Predictive Sequence Learning (PSL) which has previously been proposed as a learning algorithm for robot control. One strength of the proposed recognition methods is that the model PSL builds to recognize behaviors is ...
Erik Alexander Billing +2 more
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Reward Learning from Narrated Demonstrations [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Humans effortlessly "program" one another by communicating goals and desires in natural language. In contrast, humans program robotic behaviours by indicating desired object locations and poses to be achieved, by providing RGB images of goal configurations, or supplying a demonstration to be imitated. None of these methods generalize across environment
Hsiao-Yu Tung +3 more
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Learning from Demonstration for Hydraulic Manipulators [PDF]
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 This paper presents, for the first time, a method for learning in-contact tasks from a teleoperated demonstration with a hydraulic manipulator. Due to the use of extremely powerful hydraulic manipulator, a force-reflected bilateral teleoperation is the most reasonable method of giving a human demonstration.
Koivumäki, Janne +5 more
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Accelerating Robot Trajectory Learning for Stochastic Tasks
IEEE Access, 2020 Learning from demonstration provides ways to transfer knowledge and skills from humans to robots. Models based solely on learning from demonstration often have very good generalization capabilities but are not completely accurate when adapting to new ...
Josip Vidakovic +4 more
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Learning Task Priorities from Demonstrations [PDF]
IEEE Transactions on Robotics, 2019 Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space ...
João Silvério +3 more
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