1 citations · 1 across the 3 of their papers we have counts for
4 papers
Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
Bowen Li, Mayank Mishra, Y. Isabel Liu +7
Intelligent robots should not only recover from failures, but also acquire the abstract knowledge needed to avoid them in the future. While reinforcement learning (RL) can learn re…
KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
Yixuan Huang, Bowen Li, Vaibhav Saxena +9
Robotic systems that interact with the physical world must reason about kinematic and dynamic constraints imposed by their own embodiment, their environment, and the task at hand.…
Adaptive Language-Guided Abstraction from Contrastive Explanations
Andi Peng, Belinda Z. Li, Ilia Sucholutsky +4
Many approaches to robot learning begin by inferring a reward function from a set of human demonstrations. To learn a good reward, it is necessary to determine which features of th…
Preference-Conditioned Language-Guided Abstraction
Andi Peng, Andreea Bobu, Belinda Z. Li +5
Learning from demonstrations is a common way for users to teach robots, but it is prone to spurious feature correlations. Recent work constructs state abstractions, i.e. visual rep…