Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulation

• URL: http://arxiv.org/abs/2508.11204v1
• Date: Fri, 15 Aug 2025 04:30:01 GMT
• Title: Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulation
• Authors: Hongbin Lin, Juan Rojas, Kwok Wai Samuel Au,
• Abstract summary: Sampling efficiency is critical for deploying visuomotor learning in real-world robotic manipulation.<n>We introduce a novel formulation of the partially observable Markov decision process (POMDP) that incorporates the non-isometric symmetry structures.<n>We integrate MEA with offline reinforcement learning to enhance sampling efficiency, and introduce a voxel-based visual representation.
• Score: 3.842041548518154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sampling efficiency is critical for deploying visuomotor learning in real-world robotic manipulation. While task symmetry has emerged as a promising inductive bias to improve efficiency, most prior work is limited to isometric symmetries -- applying the same group transformation to all task objects across all timesteps. In this work, we explore non-isometric symmetries, applying multiple independent group transformations across spatial and temporal dimensions to relax these constraints. We introduce a novel formulation of the partially observable Markov decision process (POMDP) that incorporates the non-isometric symmetry structures, and propose a simple yet effective data augmentation method, Multi-Group Equivariance Augmentation (MEA). We integrate MEA with offline reinforcement learning to enhance sampling efficiency, and introduce a voxel-based visual representation that preserves translational equivariance. Extensive simulation and real-robot experiments across two manipulation domains demonstrate the effectiveness of our approach.

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