ReEnvision: Future-Guided Selective Action Repair for Long-Horizon Manipulation
ReEnvision: Future-Guided Selective Action Repair for Long-Horizon Manipulation
ReEnvision is a framework for long-horizon robotic manipulation that addresses execution deviations by selectively repairing affected actions using future-guided belief updates. It improves success rates on multiple benchmarks and real-world tasks, demonstrating the benefits of localized repair and preserving valid actions during execution.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Reported success rate
88.8%
Reported success rate
82.8%
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
affects each unexecuted action. The resulting belief revision enables selective repair of only the affected actions within a cached action chunk, preserving valid actions and reserving full replanning for severe deviations. On CALVIN, L-CALVIN, and RLBench, ReEnvision achieves 86.6% 5-step success, improves 10-step success from 56% to 63%, and reaches an average success rate of 88.8%, respectively. On five real-worl
Open source S4
d manipulation tasks, it achieves an average success rate of 82.8%. Controlled repair experiments further demonstrate the benefits of localizing affected actions and preserving valid ones during execution.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org