VARP: A Visual Adaptive Rearrangement Planner for Non-Monotone Object Rearrangement Planning
Overview
This research presents VARP, a visual adaptive rearrangement planner that learns non-monotone object rearrangement directly from visual state representations. The system predicts pick-and-place actions using current and goal visual states, incorporating an Action Refinement Strategy (ARS) to enhance feasibility and a Progress Enforcement Strategy (PES) to prevent stagnation. VARP adapts online to disturbances without replanning or combinatorial search. In challenging benchmarks, it achieves a 98.3% success rate with 0.4 seconds planning time per task, maintains 97.5% success under execution-time disturbances, and reaches 94.5% under continuous severe disturbances, while generalizing to unseen object counts.
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“rom the latest visual state representations, VARP adapts online to disturbances without replanning from scratch or performing online combinatorial search. In challenging rearrangement benchmarks, VARP achieves a 98.3\% success rate with only 0.4 s planning time per task, 97.5\% success under execution-time disturbances, and 94.5\% under continuous severe disturbances, while generalizing effectively to unseen object c”
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- arXiv Robotics — research abstractsSignalVARP: A Visual Adaptive Rearrangement Planner for Non-Monotone Object Rearrangement Planning
This research presents VARP, a visual adaptive rearrangement planner that learns non-monotone object rearrangement directly from visual state representations. The system predicts pick-and-place actions using current and goal visual states, incorporating an Action Refinement Strategy (ARS) to enhance feasibility and a Progress Enforcement Strategy (PES) to prevent stagnation. VARP adapts online to disturbances without replanning or combinatorial search. In challenging benchmarks, it achieves a 98.3% success rate with 0.4 seconds planning time per task, maintains 97.5% success under execution-time disturbances, and reaches 94.5% under continuous severe disturbances, while generalizing to unseen object counts.
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