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HODAgent introduces a System-2 architecture enabling humanoid robots in service settings to handle dynamic requests, revise tasks mid-execution, and verify outcomes through a unified simulation-to-reality interface. By integrating hierarchical memory with visual language models, the system achieves significant improvements on embodied benchmarks while maintaining coherent interaction states during complex episodes. This advancement marks a critical step toward responsive physical agents capable of adapting to real-world human interactions without platform-specific control constraints.
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HODAgent: Towards On-Demand, Responsive Humanoids for Physical World Human Interaction

arXiv2 min read

arXiv:2608.17584v1 Announce Type: new Abstract: We propose HODAgent, a System-2 embodied agent for humanoid robots in service settings, addressing situated intent, responsive execution, task revision, and outcome verification. Its semi-duplex architecture integrates an Env-Interactor, Planner, Executor, and hierarchical Memory to maintain coherent interaction, planning, and task state during service episodes. This allows handling new requests during motion, retaining progress, revising actions, and grounding closure in execution outcomes. A shared interface connects simulation and physical robots (Unitree G1), isolating platform-specific control. In an interactive simulation with 164 cases, HODAgent achieves 84.8% and 91.5% Joint Success under two VLM backbones, outperforming baselines by 9.8 and 18.9 points. On physical robots, pass rates are 92% (atomic), 72% (composite), and 63.3% (complete tasks). On multiple embodied benchmarks, it improves over baselines by 0.7-9.0 points. Results show a unified System-2 agent enables adaptive humanoid service across simulation and reality.

Abstract:We propose HODAgent, a System-2 embodied agent for humanoid robots in service settings, addressing situated intent, responsive execution, task revision, and outcome verification. Its semi-duplex architecture integrates an Env-Interactor, Planner, Executor, and hierarchical Memory to maintain coherent interaction, planning, and task state during service episodes. This allows handling new requests during motion, retaining progress, revising actions, and grounding closure in execution outcomes. A shared interface connects simulation and physical robots (Unitree G1), isolating platform-specific control. In an interactive simulation with 164 cases, HODAgent achieves 84.8% and 91.5% Joint Success under two VLM backbones, outperforming baselines by 9.8 and 18.9 points. On physical robots, pass rates are 92% (atomic), 72% (composite), and 63.3% (complete tasks). On multiple embodied benchmarks, it improves over baselines by 0.7-9.0 points. Results show a unified System-2 agent enables adaptive humanoid service across simulation and reality.

Robotics (cs.RO)

arXiv:2608.17584 [cs.RO]

(or arXiv:2608.17584v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.17584 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 232 words

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