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The paper identifies a critical bottleneck where excessive reliance on sim2real transfer creates simulator lock-in and restricts policy exploration due to rigid real-world constraints. It proposes a "sim2sim2real" paradigm that uses robot kinematics as the primary design constraint to resolve these misaligned incentives. This approach offers a practical pathway for researchers to balance simulation efficiency with the flexibility needed for robust hardware deployment.
Too Much of a Good Thing: When sim2real Efforts Impede Policy Learning (And What to Do About It)
arXiv:2606.02636v1 Announce Type: cross Abstract: While sim2real efforts are necessary for effective policy transfer to hardware, there is such a thing as too much of a good thing. We argue that sim2real efforts have led to misaligned incentives with policy learning, resulting in simulator lock in and poor policy exploration due to the unreasonable constraints imposed by the real world. We offer a diagnosis and explanation of the current status of the problem, and propose a potential solution via a sim2sim2real paradigm that leverages the robot's kinematics as the sole design constraint.
Abstract:While sim2real efforts are necessary for effective policy transfer to hardware, there is such a thing as too much of a good thing. We argue that sim2real efforts have led to misaligned incentives with policy learning, resulting in simulator lock in and poor policy exploration due to the unreasonable constraints imposed by the real world. We offer a diagnosis and explanation of the current status of the problem, and propose a potential solution via a sim2sim2real paradigm that leverages the robot's kinematics as the sole design constraint.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2606.02636 [cs.RO]
(or arXiv:2606.02636v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.02636 Focus to learn more arXiv-issued DOI via DataCite
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