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Probabilistic model-based imitation learning [PDF]
Adaptive Behavior, 2013 Efficient skill acquisition is crucial for creating versatile robots. One intuitive way to teach a robot new tricks is to demonstrate a task and enable the robot to imitate the demonstrated behavior. This approach is known as imitation learning. Classical methods of imitation learning, such as inverse reinforcement learning or behavioral cloning ...
Englert, P +3 more
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NeuroImageImitation learning and co-presence learning are common forms of social learning. However, the effects of these two types of learning on acquiring word formation rules have gone relatively underexplored, particularly in the context of adult social ...
Yujing Shen +3 more
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Dossier Imitation - Introduction générale
Revue de Primatologie, 2009 This special issue is interested in the ontogeny and the phylogeny of imitation. We have invited experts in developmental psychology and in primatology to discuss the definition of imitation in Human, its existence in non human primate species and to ...
Odile Petit, Olivier Pascalis
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Hybrid Trajectory and Force Learning of Complex Assembly Tasks: A Combined Learning Framework
IEEE Access, 2021 Complex assembly tasks involve nonlinear and low-clearance insertion trajectories with varying contact forces at different stages. For a robot to solve these tasks, it requires a precise and adaptive controller which conventional force control methods ...
Yan Wang +3 more
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Generative Adversarial Network for Imitation Learning from Single Demonstration
مجلة بغداد للعلوم, 2021 Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations.
Tho Nguyen Duc +3 more
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Imitation learning for task allocation [PDF]
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010 At the heart of multi-robot task allocation lies the ability to compare multiple options in order to select the best. In some domains this utility evaluation is not straightforward, for example due to complex and unmodeled underlying dynamics or an adversary in the environment. Explicitly modeling these extrinsic influences well enough so that they can
Duvallet, Felix, Stentz, Anthony
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MachinesIn imitation learning between humans and robots, the embodiment gap is a key challenge. By focusing on a specific body part and compensating for the rest according to the robot’s size, the embodiment gap can be overcome. In this paper, we analyze dynamic
Yoshiki Tsunekawa, Kosuke Sekiyama
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, 2020 In analogy to compressed sensing, which allows sample-efficient signal reconstruction given prior knowledge of its sparsity in frequency domain, we propose to utilize policy simplicity (Occam's Razor) as a prior to enable sample-efficient imitation learning.
Zhao, Nathan, Lou, Beicheng
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MachinesIn imitation learning with the embodiment gap, directly transferring human motions to robots is challenging due to differences in body structures. Therefore, it is necessary to reconstruct human motions in accordance with each robot’s embodiment.
Yoshiki Tsunekawa +2 more
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Rethinking plastic waste: innovations in enzymatic breakdown of oil‐based polyesters and bioplastics
FEBS Open Bio, EarlyView.Plastic pollution remains a critical environmental challenge, and current mechanical and chemical recycling methods are insufficient to achieve a fully circular economy. This review highlights recent breakthroughs in the enzymatic depolymerization of both oil‐derived polyesters and bioplastics, including high‐throughput protein engineering, de novo ...
Elena Rosini +2 more
wiley +1 more source