Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
This addresses the challenge of open-ended training for embodied intelligent agents in robotics, though it is incremental in applying existing methods to a new domain.
The paper tackles the problem of training autonomous robots to assemble multi-part physical structures using a physics-based environment with magnet blocks, and finds that large-scale reinforcement learning with graph-based policies enables agents to generalize to unseen blueprints and operate reset-free without specific training.
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.