29 citations · 72 across the 7 of their papers we have counts for
5 papers · 1 filter
GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robot
Tianli Ding, Laura Graesser, Saminda Abeyruwan +5
Learning goal conditioned control in the real world is a challenging open problem in robotics. Reinforcement learning systems have the potential to learn autonomously via trial-and…
Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning
Brian Ichter, Pierre Sermanet, Corey Lynch
Long-horizon planning in realistic environments requires the ability to reason over sequential tasks in high-dimensional state spaces with complex dynamics. Classical motion planni…
Learning to Play by Imitating Humans
Rostam Dinyari, Pierre Sermanet, Corey Lynch
Acquiring multiple skills has commonly involved collecting a large number of expert demonstrations per task or engineering custom reward functions. Recently it has been shown that…
Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos
Ajay Kumar Tanwani, Pierre Sermanet, Andy Yan +3
Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we lear…
Learning Latent Plans from Play
Corey Lynch, Mohi Khansari, Ted Xiao +4
Acquiring a diverse repertoire of general-purpose skills remains an open challenge for robotics. In this work, we propose self-supervising control on top of human teleoperated play…