BusyBot: Learning to Interact, Reason, and Plan in a BusyBoard EnvironmentDownload PDF

16 Jun 2022, 10:45 (modified: 27 Oct 2022, 02:40)CoRL 2022 PosterReaders: Everyone
Student First Author: yes
Keywords: Manipulation, Scene Understanding, Learning Environment
TL;DR: We propose a toy-inspired relational environment, BusyBoard, and a learning framework, BusyBot, for embodied AI agents to acquire interaction, reasoning, and planning skills.
Abstract: We introduce BusyBoard, a toy-inspired robot learning environment that leverages a diverse set of articulated objects and inter-object functional relations to provide rich visual feedback for robot interactions. Based on this environment, we introduce a learning framework, BusyBot, which allows an agent to jointly acquire three fundamental capabilities (interaction, reasoning, and planning) in an integrated and self-supervised manner. With the rich sensory feedback provided by BusyBoard, BusyBot first learns a policy to efficiently interact with the environment; then with data collected using the policy, BusyBot reasons the inter-object functional relations through a causal discovery network; and finally by combining the learned interaction policy and relation reasoning skill, the agent is able to perform goal-conditioned manipulation tasks. We evaluate BusyBot in both simulated and real-world environments, and validate its generalizability to unseen objects and relations.
Supplementary Material: zip
Website: https://busybot.cs.columbia.edu/
Code: https://github.com/columbia-ai-robotics/busybot
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