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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.02526 |
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| _version_ | 1866915531657838592 |
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| author | H, Anamika J Muraleedharan, Anujith |
| author_facet | H, Anamika J Muraleedharan, Anujith |
| contents | Robots manipulating in changing environments must act on percepts that are late, noisy, or stale. We present U-LAG, a mid-execution goal-retargeting layer that leaves the low-level controller unchanged while re-aiming task goals (pre-contact, contact, post) as new observations arrive. Unlike motion retargeting or generic visual servoing, U-LAG treats in-flight goal re-aiming as a first-class, pluggable module between perception and control. Our main technical contribution is UAR-PF, an uncertainty-aware retargeter that maintains a distribution over object pose under sensing lag and selects goals that maximize expected progress. We instantiate a reproducible Shift x Lag stress test in PyBullet/PandaGym for pick, push, stacking, and peg insertion, where the object undergoes abrupt in-plane shifts while synthetic perception lag is injected during approach. Across 0-10 cm shifts and 0-400 ms lags, UAR-PF and ICP degrade gracefully relative to a no-retarget baseline, achieving higher success with modest end-effector travel and fewer aborts; simple operational safeguards further improve stability. Contributions: (1) UAR-PF for lag-adaptive, uncertainty-aware goal retargeting; (2) a pluggable retargeting interface; and (3) a reproducible Shift x Lag benchmark with evaluation on pick, push, stacking, and peg insertion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | U-LAG: Uncertainty-Aware, Lag-Adaptive Goal Retargeting for Robotic Manipulation H, Anamika J Muraleedharan, Anujith Robotics Robots manipulating in changing environments must act on percepts that are late, noisy, or stale. We present U-LAG, a mid-execution goal-retargeting layer that leaves the low-level controller unchanged while re-aiming task goals (pre-contact, contact, post) as new observations arrive. Unlike motion retargeting or generic visual servoing, U-LAG treats in-flight goal re-aiming as a first-class, pluggable module between perception and control. Our main technical contribution is UAR-PF, an uncertainty-aware retargeter that maintains a distribution over object pose under sensing lag and selects goals that maximize expected progress. We instantiate a reproducible Shift x Lag stress test in PyBullet/PandaGym for pick, push, stacking, and peg insertion, where the object undergoes abrupt in-plane shifts while synthetic perception lag is injected during approach. Across 0-10 cm shifts and 0-400 ms lags, UAR-PF and ICP degrade gracefully relative to a no-retarget baseline, achieving higher success with modest end-effector travel and fewer aborts; simple operational safeguards further improve stability. Contributions: (1) UAR-PF for lag-adaptive, uncertainty-aware goal retargeting; (2) a pluggable retargeting interface; and (3) a reproducible Shift x Lag benchmark with evaluation on pick, push, stacking, and peg insertion. |
| title | U-LAG: Uncertainty-Aware, Lag-Adaptive Goal Retargeting for Robotic Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.02526 |