Unverified50% confidenceFactExact time
There is a sim-to-real gap: friction, mass and sensor noise in simulation deviate from the real world, so policy transfer often fails; domain randomization forces the policy to learn robust behavior by randomly perturbing physical parameters and visual appearance during training.
1
Sources
50%
Confidence
Long-term
Relevance
11/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe real world is non-stationary, and a policy's performance during training may deteriorate sharply after the environment undergoes a distribution shift.76% similarUnverifiedThe Sim-to-Real Gap refers to policies trained in simulation failing due to factors such as real-world sensor noise and actuator latency; domain randomization is a common method for narrowing this gap.75% similarVerifiedThe hard-to-bridge gap between the simulated world and the real world, known as the Reality Gap, stems from fine-grained discrepancies such as motor response delays, joint friction, sensor noise, and ground material differences75% similarUnverifiedThe most valuable data for model training is often hard examples (such as the critical friction state when an object is about to slip); these edge cases are dangerous and difficult to reproduce in real-world collection, leading to pervasive imbalanced distributions and incomplete coverage in open-source tactile datasets.75% similarVerifiedDrift detection involves a trade-off: setting the threshold too sensitively leads to frequent false alarms and unnecessary retraining, while setting it too loosely may allow model performance to continuously degrade without being detected.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1123059API
curl https://kongchang.com/api/v1/knowledge/claims/1123059MCP
get_claim(id=1123059)