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We introduce BusyBoard, a toy-inspired robot learning environment thatleverages a diverse set of articulated objects and inter-object functionalrelations to provide rich visual feedback for robot interactions. Based on thisenvironment, we introduce a learning framework, BusyBot, which allows an agentto jointly acquire three fundamental capabilities (interaction, reasoning, andplanning) in an integrated and self-supervised manner. With the rich sensoryfeedback provided by BusyBoard, BusyBot first learns a policy to efficientlyinteract with the environment; then with data collected using the policy,BusyBot reasons the inter-object functional relations through a causaldiscovery network; and finally by combining the learned interaction policy andrelation reasoning skill, the agent is able to perform goal-conditionedmanipulation tasks. We evaluate BusyBot in both simulated and real-worldenvironments, and validate its generalizability to unseen objects andrelations. Video is available at https://youtu.be/EJ98xBJZ9ek.

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