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This work was partially supported by the United States Department of Agriculture (USDA) under Grant Nos. 2023-67021-39072 (Y.S.), 2023-67022-39074 (W.S.), 2023-67022-39075 (D.W.), and 2024-67021-42878 (Y.S.), and by the National Science Foundation (NSF) under Grant No. 2423068 (Y.S.).†
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Enforcing symmetry equivariance in bimanual robot learning yields policies that are not only more performant but also significantly more robust to distribution shifts, a crucial step towards reliable real-world deployment.
