Deciding If, When, and Where to Replan for Robust Manipulation
Deciding If, When, and Where to Replan for Robust Manipulation
This research proposes Robust-TAMP, a task and motion planning framework that dynamically decides whether to replan during manipulation tasks. The method improves success rates in both simulated and real-world environments, outperforming existing baselines by 47.1 percentage points. The framework allows for inserting corrective actions into existing plans based on urgency, and the study shows that in-context examples help planners follow multi-step procedures more effectively.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
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number of MuJoCo environments
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ther a discovery needs a replan. If replanning is needed, the robot executes unaffected actions while the FM synthesizes corrective actions. These actions are inserted into the existing plan based on urgency, either executed immediately or deferred while preserving the remaining plan. We evaluate Robust-TAMP on 14 MuJoCo environments with hidden objects, blocked placements, articulated containers, and multi-step task
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s, and on five real-world settings. Our proposed method completes 92.1% of simulated tasks, outperforming four baseline comparison architectures by 47.1 percentage points under the same settings. Ablations show that each component either raises success or lowers planner calls, planning time, and robot idle time. An extensive study across model sizes, text and vision inputs, reasoning modes, and prompting further show
Open source S4
ther a discovery needs a replan. If replanning is needed, the robot executes unaffected actions while the FM synthesizes corrective actions. These actions are inserted into the existing plan based on urgency, either executed immediately or deferred while preserving the remaining plan. We evaluate Robust-TAMP on 14 MuJoCo environments with hidden objects, blocked placements, articulated containers, and multi-step task
Open source S3
s that in-context examples help the planner follow multi-step procedures that zero-shot prompting struggles with. Our implementation code and project details are available at https://narendhiranv04.github.io/ROBUST-TAMP/.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org