aiqbench analysis
The paper identifies a critical bottleneck where excessive reliance on sim2real transfer creates simulator lock-in and restricts policy exploration due to unrealistic real-world constraints. It proposes a "sim2sim2real" paradigm that uses only kinematics as a design constraint to realign incentives and improve exploration. This approach offers a practical pathway to overcome current limitations in robotic policy learning without discarding the benefits of simulation.
Too Much of a Good Thing: When sim2real Efforts Impede Policy Learning (And What to Do About It)
arXiv:2606.02636v2 Announce Type: replace 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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Full text under the publisher's open licence · 181 words
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