Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics¶
Zhao, W., Queralta, J. P., & Westerlund, T. (2020). Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: A Survey. IEEE, 737-744.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Production-Like Testbed
- Its defining failure mode is the sim-to-real gap: the replica is always an approximation, and the bugs that matter most are often precisely the ones living in the couplings it failed to reproduce, so a green testbed breeds false confidence exactly where fidelity fell short.
This sourceIdentifies the sim-to-real gap as the mismatch that arises when policies trained in simulated approximations must transfer to physical reality.
- Its defining failure mode is the sim-to-real gap: the replica is always an approximation, and the bugs that matter most are often precisely the ones living in the couplings it failed to reproduce, so a green testbed breeds false confidence exactly where fidelity fell short.
Verification¶
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Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
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Registry ID ref:f8d073942aed · see in the full table