RESETTLE: Robotic Recovery through Disagreement-Triggered Retrieval and Efficient Corrective Control
To address these challenges, we introduce RESETTLE(Robotic rEcovery through diSagrEement-Triggered reTrievaL and Efficient Corrective Control), a model-agnostic framework that provides computationally efficient recovery at the action-execution interface of frozen robot policies.
ProofPaper ↗
Key points
- Reliable robotic manipulation requires timely intervention to correct emerging deviations and restore progress after execution errors.
- However, recovery methods based on repeated vision-language reasoning or iterative online optimization can incur substantial latency, delaying intervention.
- RESETTLE triggers recovery when two action proposals independently sampled under identical conditioning persistently disagree.
- It retrieves a same-task demonstration reference using an adapted V-JEPA encoder and combines a state-servo prior with a guarded visual residual to execute one corrective action without online trajectory optimization or additional vision-language reasoning, then returns control to the base policy.
Sources (1)
- [1]RESETTLE: Robotic Recovery through Disagreement-Triggered Retrieval and Efficient Corrective ControlarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 03:49 PM
To address these challenges, we introduce RESETTLE(Robotic rEcovery through diSagrEement-Triggered reTrievaL and Efficient Corrective Control), a model-agnostic framework that provides computationally efficient recovery at the action-execution interface of frozen robot policies.
Reliable robotic manipulation requires timely intervention to correct emerging deviations and restore progress after execution errors.
Extractive summary: sentences quoted from the sources.
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