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The paper introduces ANCHOR, a closed-loop framework for mobile manipulation that addresses inconsistencies between symbolic plans and the physical world by grounding predicates to observable geometric anchors. ANCHOR incorporates operability-aware base alignment and minimum-responsible-layer hierarchical recovery to improve long-horizon execution in dynamic environments. Results from 60 real-robot trials show that ANCHOR improves task success from 53.3% to 71.7% and achieves a 71.4% recovery rate under perturbations, highlighting the importance of physical grounding and structured failure containment.
Robots often fail at simple household tasks not because they're confused, but because their plans become detached from the messy, ever-changing physical world.
Recent advances in open-vocabulary mobile manipulation have brought robots into real domestic environments. In such settings, reliable long-horizon execution under open-set object references and frequent disturbances becomes essential. However, many failures persist. These are not caused by semantic misunderstanding but by inconsistencies between symbolic plans and the evolving physical world, manifested as three recurring limitations: (i) existing systems often rely on pre-scanned semantic maps that become inconsistent after scene changes and disturbances; (ii) they select navigation endpoints without considering downstream manipulation feasibility, causing the"arrived but inoperable"problem; and (iii) they handle anomalies through undifferentiated global replanning, which often fails to contain local errors. To address this execution inconsistency, we present ANCHOR, a physically grounded closed-loop framework that aligns symbolic reasoning with verifiable physical state during execution. ANCHOR integrates three mechanisms: (i) physically anchored task planning, which binds symbolic predicates to observable geometric anchors and re-validates them after each action; (ii) operability-aware base alignment, which ensures that navigation endpoints satisfy kinematic reachability and local collision feasibility; and (iii) minimum-responsible-layer hierarchical recovery, which localizes failures across perception, base-arm coordination, and execution layers to prevent cascading retries. Across 60 real-robot trials in previously unseen environments, ANCHOR improves task success from 53.3% to 71.7% and achieves a 71.4% recovery rate under perturbations, demonstrating that explicit physical grounding and structured failure containment are critical for robust mobile manipulation. Our project page is available at https://anchor9178.github.io/ANCHOR/ .
