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Poster
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
Seyed Kamyar Seyed Ghasemipour · Satoshi Kataoka · Byron David · Daniel Freeman · Shixiang Gu · Igor Mordatch

Thu Jul 21 03:00 PM -- 05:00 PM (PDT) @ Hall E #123
in Poster Session 3 »

Assembly of multi-part physical structures is both a valuable end product for autonomous robotics, as well as a valuable diagnostic task for open-ended training of embodied intelligent agents. We introduce a naturalistic physics-based environment with a set of connectable magnet blocks inspired by children’s toy kits. The objective is to assemble blocks into a succession of target blueprints. Despite the simplicity of this objective, the compositional nature of building diverse blueprints from a set of blocks leads to an explosion of complexity in structures that agents encounter. Furthermore, assembly stresses agents' multi-step planning, physical reasoning, and bimanual coordination. We find that the combination of large-scale reinforcement learning and graph-based policies-- surprisingly without any additional complexity -- is an effective recipe for training agents that not only generalize to complex unseen blueprints in a zero-shot manner, but even operate in a reset-free setting without being trained to do so. Through extensive experiments, we highlight the importance of large-scale training, structured representations, contributions of multi-task vs. single-task learning, as well as the effects of curriculums, and discuss qualitative behaviors of trained agents. Our accompanying project webpage can be found at: https://sites.google.com/view/learning-direct-assembly/home

Keywords: APP: Robotics · DL: Graph Neural Networks · RL: Deep RL · RL: Online · MISC: Transfer, Multitask and Meta-learning

Author Information

Seyed Kamyar Seyed Ghasemipour (University of Toronto)
Satoshi Kataoka (Google LLC)
Byron David (Google)
Daniel Freeman (Google Brain)
Shixiang Gu (Google)
Igor Mordatch (Google Brain)

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