ANCHOR: A Physically Grounded Closed-Loop Framework for Robust Home-Service Mobile Manipulation

• URL: http://arxiv.org/abs/2604.25323v1
• Date: Tue, 28 Apr 2026 07:37:39 GMT
• Title: ANCHOR: A Physically Grounded Closed-Loop Framework for Robust Home-Service Mobile Manipulation
• Abstract summary: We present ANCHOR, a closed-loop framework that aligns symbolic reasoning with verifiable physical state during execution.<n>ANCHOR improves task success from 53.3% to 71.7% and achieves a 71.4% recovery rate under perturbations.
• Score: 10.278758849939917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/ .

Related papers

• ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
  We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
  arXiv  Detail & Related papers  (2026-08-03T15:07:46Z)
• From Failures to Supervision: DynamicEnvPlan for Robust Long-Horizon Embodied Planning [11.738655774128366]
  We introduce DynamicEnvPlan, a closed-loop framework for high-level planning in dynamic environments.<n>It extends embodied task execution with humanoid agents, high-level primitive skills, structured semantic memory, and controllable perturbations.<n>The resulting traces are used for staged supervised fine-tuning, enabling the planner to learn from both nominal execution and recovery trajectories.
  arXiv  Detail & Related papers  (2026-08-01T12:09:47Z)
• Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning [60.888463449435484]
  We present Shift & Drift, a novel dual-track benchmark designed to rigorously stress-test motion planners across two critical axes of distribution shift.<n>We show that imitation learning methods achieve high scores in ID benchmarks, but suffer from persistent drift when subjected to temporally correlated actuation noise.<n>Our findings reveal an empirical trade-off between imitation fidelity and closed-loop resilience, providing the community with a rigorous benchmark to evaluate progress toward reliable deployment.
  arXiv  Detail & Related papers  (2026-07-08T18:23:48Z)
• ReactiveBFM: Reactive Closed-Loop Motion Planning Towards Universal Humanoid Whole-Body Control [54.267632784131415]
  ReactiveBFM is a real-time-loop planning-control framework for humanoids.<n>It mitigates fatal cumulative exposure bias via a scheduled prefix sampling curriculum.<n>It demonstrates unprecedented physical agility across a vast repertoire of text-conditioned closed-loop motions.
  arXiv  Detail & Related papers  (2026-06-29T14:27:27Z)
• Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy [79.09714980186278]
  Building persistent embodied agents in unstructured environments requires unified orchestration of heterogeneous tools.<n>We present OmniAct, a framework integrating a multimodal semantic planner for skill routing across unified action spaces.<n>It achieves consistent improvements in end-to-end success across all complexity levels.
  arXiv  Detail & Related papers  (2026-06-25T16:36:35Z)
• RelAfford6D: Relational 6D Affordance Graphs for Constraint-Driven Robotic Manipulation [15.618446008590409]
  We introduce RelAfford6, a training-free framework for complex articulated objects.<n>Our system deduces a semantic topology linking a primary interacting part to its physical anchor.<n>We analytically formulate downstream execution as a satisfaction problem.
  arXiv  Detail & Related papers  (2026-06-25T13:42:26Z)
• PRISM: : Planning and Reasoning with Intent in Simulated Embodied Environments [59.07829883257003]
  Built on five multi-room apartments, PRISM structures 300 human-verified tasks into three capability tiers.<n>PRISM exposes an agent-agnostic executable action API that allows arbitrary agents to be evaluated end-to-end.
  arXiv  Detail & Related papers  (2026-05-12T04:59:47Z)
• Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation [91.20850436220267]
  We introduce the Referring-Aware Visuomotor Policy (ReV)<n>ReV incorporates sparse referring points provided by a human or a high-level reasoning planner.<n>It is trained only by applying targeted perturbations to expert demonstrations.
  arXiv  Detail & Related papers  (2026-04-07T07:41:11Z)
• RoboStream: Weaving Spatio-Temporal Reasoning with Memory in Vision-Language Models for Robotics [25.944338247511215]
  VLM-based planners treat each step as an observation-to-action mapping, forcing them to reinfer scene geometry from raw pixels at every decision point.<n>We propose SStream-Temporal Fusion Tokens (TF-Tokens), which bind visual evidence to 3D geometric attributes for persistent object grounding.<n>This design enables the planner to trace causal chains and preserve object permanence under occlusion without additional training or fine-tuning.
  arXiv  Detail & Related papers  (2026-03-13T12:34:26Z)
• HaltNav: Reactive Visual Halting over Lightweight Topological Priors for Robust Vision-Language Navigation [1.774434289475737]
  Vision-and-Language Navigation (VLN) is shifting from rigid, step-by-step instruction following toward open-vocabulary, goal-oriented autonomy.<n>We propose a hierarchical navigation framework HaltNav that couples the robust global planning of osmAG with the local exploration and instruction-grounding capability of VLN.
  arXiv  Detail & Related papers  (2026-03-13T06:22:35Z)
• RADAR: Closed-Loop Robotic Data Generation via Semantic Planning and Autonomous Causal Environment Reset [48.645870795753105]
  We introduce Robust Autonomous Data Acquisition for Robotics (RADAR)<n>RADAR is a fully autonomous, closed-loop data generation engine that completely removes human intervention from the collection cycle.<n>In simulation, our framework achieves up to 90% success rates on complex, long-horizon tasks.
  arXiv  Detail & Related papers  (2026-03-12T11:18:52Z)
• UniManip: General-Purpose Zero-Shot Robotic Manipulation with Agentic Operational Graph [23.060488218180936]
  We present UniManip, a framework grounded in a Bi-level Agentic Operational Graph (AOG)<n>By coupling a high-level Agentic Layer for task orchestration with a low-level Scene Layer for dynamic state representation, the system continuously aligns abstract planning with geometric constraints.<n>Experiments validate the system's robust zero-shot capability on unseen objects and tasks, demonstrating a 22.5% and 25.0% higher success rate compared to state-of-the-art VLA and hierarchical baselines.
  arXiv  Detail & Related papers  (2026-02-13T16:47:26Z)
• V-CAGE: Context-Aware Generation and Verification for Scalable Long-Horizon Embodied Tasks [6.820118518027692]
  V-CAGE is a closed-loop framework for generating semantically aligned manipulation datasets at scale.<n>We propose a context-aware instantiation mechanism that enforces geometric consistency during scene synthesis.<n>We also employ a hierarchical instruction decomposition module to bridge the gap between abstract intent and low-level control.
  arXiv  Detail & Related papers  (2026-01-21T16:41:51Z)
• Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs [50.075587392477935]
  We conduct the first large-scale empirical study of 705 real-world failures from the open-source DeepSeek, Llama, and Qwen ecosystems.<n>Our analysis reveals a paradigm shift: white-box orchestration relocates the reliability bottleneck from model algorithmic defects to the systemic fragility of the deployment stack.
  arXiv  Detail & Related papers  (2026-01-20T06:42:56Z)
• Scene Graph-Guided Proactive Replanning for Failure-Resilient Embodied Agent [9.370683025542686]
  We present a proactive replanning framework that detects and corrects failures at subtask boundaries.<n>Experiments in the AI2-THOR simulator demonstrate that our approach detects semantic and spatial mismatches before execution failures occur.
  arXiv  Detail & Related papers  (2025-08-15T07:48:51Z)
• Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection [56.66677293607114]
  We propose Code-as-Monitor (CaM) for both open-set reactive and proactive failure detection.<n>To enhance the accuracy and efficiency of monitoring, we introduce constraint elements that abstract constraint-related entities.<n>Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances.
  arXiv  Detail & Related papers  (2024-12-05T18:58:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.