arXiv：cs.LG· Mohammad Khoshnazar, Mohammad Dehghani Tezerjani, Zhiyuan Gao, Deyuan Qu, Max Gandyra, Yanxiang Zhan, Mehreen Naeem, Andrew Melnik, Jeroen Schafer, Qing Yang, Michael Beetz·· 3 小时前
RAFC：面向时间鲁棒机器人操作的可信度感知未来条件化
Reliability-Aware Future Conditioning for Temporally Robust Robot Manipulation
AI 导读
针对生成未来视频与机器人实际所处阶段错位会导致有害引导的问题，研究者提出 Reliability-Aware Future Conditioning（RAFC），将其视为控制问题而非生成问题，在每一步估计对收到片段的信任程度并选择邻近时间假设，否则回退到静态分支，仅靠任务奖励学习。
正文
Authors:Mohammad Khoshnazar, Mohammad Dehghani Tezerjani, Zhiyuan Gao, Deyuan Qu, Max Gandyra, Yanxiang Zhan, Mehreen Naeem, Andrew Melnik, Jeroen Schafer, Qing Yang, Michael Beetz
Abstract:A generated video of a task the robot is about to perform is useful guidance only if it depicts the phase the robot is actually in. We show that temporal misalignment can turn a task-consistent generated future into actively harmful guidance. On CALVIN, a five-frame early shift nearly erases the benefit of generated futures, reducing success from 81.3% to 54.8% against 54.0% without futures; imposed timing shifts reduce it even further to 34.2%, 19.8 points below the future-free policy. We introduce Reliability-Aware Future Conditioning (RAFC), which treats this as a control problem rather than a generation problem. At every step, RAFC estimates how far to trust the received clip and which nearby temporal hypothesis to prefer, falling back toward a static branch when neither fits, and it learns both from task reward alone without shift labels or alignment supervision. RAFC sits on top of Future-Experience Conditioning (FEC), which builds the clip once from task grounding, a robot-free digital-twin rollout, and mask-free video diffusion. Under deliberately off-grid phase shifts and rate mismatch, RAFC substantially improves success under temporal mismatch. Candidate ensembling accounts for most of the recovery near alignment, while learned reliability adds a further 7.0 percentage points over uniform averaging of the identical candidate bank under off-grid shifts. The gain holds on the evaluated task sets and survives on a Franka under natural timing mismatch nobody imposed, where aggregate success rises from 26.7% to 56.7%. All resources will be made publicly available. this https URL.
| Subjects: | Robotics (cs.RO); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11956 [cs.RO] |
| (or arXiv:2610.11956v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11956 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mohammad Khoshnazar [view email]
[v1]
Thu, 8 Oct 2026 13:40:48 UTC (12,207 KB)
来源：arXiv：cs.LG · arxiv.org