It Takes Two to Tangram: Multi-Agent Object Centric Reinforcement Learning for Assembly
Overview
This paper introduces an object-centric reinforcement learning framework where each assembly part is treated as an agent within a graph neural network. The system learns shared policies for continuous control, predicting translational and angular velocities to avoid collisions and deadlocks during planar Tangram assembly. Using procedural generation for diverse configurations, the authors evaluate relational structure and multi-agent optimization in simulation before transferring the policy to sequential execution on a real robot, decoupling high-level coordination from low-level physical control.
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Environment: simulation
“ace. Procedural generation of target configurations and randomized initial arrangements support training and evaluation across diverse assembly problems. Our experiments isolate the roles of relational structure and multi-agent optimization, and compare learned control with sampling-based planning. We demonstrate concurrent assembly in simulation and transfer the policy to sequential execution on a real robot, as our”
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- arXiv Robotics — research abstractsSignalIt Takes Two to Tangram: Multi-Agent Object Centric Reinforcement Learning for Assembly
This paper introduces an object-centric reinforcement learning framework where each assembly part is treated as an agent within a graph neural network. The system learns shared policies for continuous control, predicting translational and angular velocities to avoid collisions and deadlocks during planar Tangram assembly. Using procedural generation for diverse configurations, the authors evaluate relational structure and multi-agent optimization in simulation before transferring the policy to sequential execution on a real robot, decoupling high-level coordination from low-level physical control.
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