Results 11 to 20 of about 96,046 (256)
Learning from Demonstration without Demonstrations [PDF]
2021 IEEE International Conference on Robotics and Automation (ICRA), 2021 International Conference on Robotics and Automation (ICRA), 2021.
Tom Blau, Philippe Morere, Gilad Francis
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Programming Fine Manufacturing Tasks on Collaborative Robots: A Case Study on Industrial Gluing [PDF]
Modeling, Identification and Control, 2023 This paper explores programming of fine manufacturing tasks using collaborative robots. We conduct a case study based on an industrial gluing task, comparing two programming approaches: Learning from Demonstration (LfD) and Computer-Aided Manufacturing ...
Daniel Schäle +2 more
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Leveraging Demonstrations for Learning the Structure and Parameters of Hierarchical Task Networks
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Hierarchical Task Networks (HTNs) are a common formalism for automated planning, allowing to leverage the hierarchical structure of many activities. While HTNs have been used in many practical applications, building a complete and efficient HTN model ...
Philippe Hérail, Arthur Bit-Monnot
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Learning From Sparse Demonstrations
IEEE Transactions on Robotics, 2023 This is a preprint.
Wanxin Jin +4 more
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Towards Fully Autonomous Negative Obstacle Traversal via Imitation Learning Based Control
Robotics, 2022 Current research in experimental robotics has had a focus on traditional, cost-based, navigation methods. These methods ascribe a value of utility for occupying certain locations in the environment.
Brian César-Tondreau +3 more
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Learning From Demonstration in the Wild [PDF]
2019 International Conference on Robotics and Automation (ICRA), 2019 Accepted to the IEEE International Conference on Robotics and Automation (ICRA) 2019; extended version with ...
Feryal M. P. Behbahani +10 more
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Contrastive Learning from Demonstrations
CoRR, 2022 This paper presents a framework for learning visual representations from unlabeled video demonstrations captured from multiple viewpoints. We show that these representations are applicable for imitating several robotic tasks, including pick and place. We optimize a recently proposed self-supervised learning algorithm by applying contrastive learning to
André Correia, Luís A. Alexandre
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Improving Learning from Demonstrations by Learning from Experience
CoRR, 2021 How to make imitation learning more general when demonstrations are relatively limited has been a persistent problem in reinforcement learning (RL). Poor demonstrations lead to narrow and biased date distribution, non-Markovian human expert demonstration makes it difficult for the agent to learn, and over-reliance on sub-optimal trajectories can make ...
Haofeng Liu +3 more
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Learning Constraints from Demonstrations [PDF]
, 2020 We extend the learning from demonstration paradigm by providing a method for learning unknown constraints shared across tasks, using demonstrations of the tasks, their cost functions, and knowledge of the system dynamics and control constraints. Given safe demonstrations, our method uses hit-and-run sampling to obtain lower cost, and thus unsafe ...
Glen Chou, Dmitry Berenson, Necmiye Ozay
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IEEE Access, 2021 Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems. Despite the large body of research works already reported, current key technological challenges include those of ...
Georgios Th. Papadopoulos +2 more
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