Organizations: The authors are with Beijing Institute of Technology.
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/ .
Bridging abstract semantics and precise physical control remains a fundamental challenge in open-world robotic manipulation. While recent data-driven policies show promise, their reliance on isolated contact points or latent affordance embeddings lacks the rigorous kinematic constraints necessary for complex articulated objects.To overcome the limitation, we introduce RelAfford6D, a novel training-free framework centered on a Relational 6D Affordance Graph. Given a free-form instruction, our system deduces a semantic topology linking a primary interacting part to its physical anchor. By elevating these topological nodes into precise metric SE(3) poses via vision foundation models, we analytically formulate downstream execution as a kinematic constraint satisfaction problem. The robot synthesizes continuous trajectories by tracking strictly defined physical manifolds (e.g., revolute or prismatic orbits). Coupled with a closed-loop tracking mechanism for dynamic replanning against disturbances, our physically grounded approach achieves superior zero-shot success rates, cross-category generalization and execution robustness in both simulation and the real world environments, outperforming existing data-driven baselines.
Although robotic manipulation has made significant progress, reliable execution remains challenging because task failures are inevitable in dynamic and unstructured environments. To handle such failures, existing frameworks typically follow a stepwise detect-reason-recover pipeline, which often incurs high latency and limited robustness due to delayed reasoning and reactive planning. Inspired by the human capability to anticipate and proactively plan for potential failures, we introduce AgentChord, an agentic system that models a manipulation task as a directed task graph. Before execution, this graph is enriched with anticipatory recovery branches that specify context-aware corrective behaviors, enabling immediate and targeted responses when failures occur. Specifically, AgentChord operates through a choreography of specialized agents: a composer that structures the nominal task graph, an arranger that augments the graph with anticipatory recovery branches, and a conductor that compiles and coordinates executable transitions using low-latency monitors to detect deviations and trigger pre-compiled recoveries without re-planning. Empirical studies on diverse long-horizon bimanual manipulation tasks demonstrate that AgentChord substantially improves success rates and execution efficiency, advancing the reliability and autonomy of real-world robotic systems. The project page is available at: https://shengxu.net/AgentChord/.
Multi-robot systems provide the parallelism and redundancy necessary for long-horizon tasks, while Large Language Models (LLMs) offer the reasoning capabilities to decompose these objectives into actionable plans. However, effectively grounding this high-level reasoning in physical multi-robot execution remains an open challenge. Existing LLM-based approaches fall mainly into two categories: Single-robot methods achieve robust contact-rich manipulation but lack the coordination mechanisms required for tasks spanning multiple workspaces. Current multi-robot frameworks focus on high-level planning, often treating manipulation as an idealized primitive that fails to account for real-world execution uncertainties. To address this, we propose a hierarchical closed-loop agentic LLM-based framework to ensure robust multi-robot manipulation. Our system consists of three specialized agents: the Planning Agent decomposes instructions into allocated sub-tasks, the Manipulation Agent for each robot executes actions via adaptive tool use, and the Verification Agent closes the loop by monitoring physical outcomes and feeding back semantic corrections. Extensive real-world experiments demonstrate that our framework achieves superior success rates, ensures robust adaptability ranging from single to cross workspace manipulation, and offers a generalizable approach for diverse manipulation tasks.