i-sim2real: Reinforcement learning of robotic policies in tight human-robot interaction loops

SW Abeyruwan, L Graesser… - … on Robot Learning, 2023 - proceedings.mlr.press
Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to
train policies in simulation enables safe exploration and large-scale data collection quickly …
被引用次数：40 相关文章

i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops

SW Abeyruwan, L Graesser, DB D'Ambrosio… - 6th Annual Conference … - openreview.net
Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to
train policies in simulation enables safe exploration and large-scale data collection quickly …

i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops

S Abeyruwan, L Graesser, DB D'Ambrosio… - arXiv preprint arXiv …, 2022 - arxiv.org
Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to
train policies in simulation enables safe exploration and large-scale data collection quickly …

i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops

S Abeyruwan, L Graesser, DB D'Ambrosio… - arXiv e …, 2022 - ui.adsabs.harvard.edu
Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to
train policies in simulation enables safe exploration and large-scale data collection quickly …

i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops

SW Abeyruwan, L Graesser… - … on Robot Learning, 2023 - proceedings.mlr.press
Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to
train policies in simulation enables safe exploration and large-scale data collection quickly …

i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops

SW Abeyruwan, L Graesser, DB D'Ambrosio, A Singh… - research.google
Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to
train policies in simulation enables safe exploration and large-scale data collection quickly …

i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops

SW Abeyruwan, L Graesser, DB D'Ambrosio, A Singh… - research.google
Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to
train policies in simulation enables safe exploration and large-scale data collection quickly …