Addressing the Orchestration Gap in Generalist Robots via Physical Agency
Researchers introduce Pigey, a physical agent orchestrator that decomposes robotic capabilities into a high-level manager and low-level policy, achieving over 4x improvement on LIBERO-PRO and near-100% success on real-world reasoning tasks.
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[Submitted on 23 Jul 2026]
Title:Addressing the Orchestration Gap in Generalist Robots via Physical Agency
View a PDF of the paper titled Addressing the Orchestration Gap in Generalist Robots via Physical Agency, by Liane Galanti and 2 other authors
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Abstract:General-purpose robots need to reason about their actions, combining perception, world knowledge, planning, success detection, recovery, and low-level control. Today's state-of-the-art models attempt to combine all these capabilities into the learned policy via large-scale pre-training. Instead, we show that these capabilities can be decomposed into a general language-conditioned policy/control agent and a high-level agent manager/orchestrator. Rather than training policies to reason via pre-training, we build a closed-loop physical agent orchestrator that can do high-level planning, decompose the goal into achievable subgoals, command low-level motor commands, track and verify the outcome from low-level observations, and recover from failures. Our Physical Agency orchestrator (Pigey) can control existing vision-language-action (VLA) policies as well as parametrized skills to solve complex reasoning tasks in the real world, without any additional data collection or post-training. We evaluate Pigey extensively across simulation benchmarks and challenging real-world robotic manipulation tasks, and demonstrate significant performance improvements over existing generalist policies. On LIBERO-PRO, Pigey advances the state-of-the-art by over 4x (12.8% -> 53.3%) with no task-specific fine-tuning. On a real robot, Pigey lifts the frozen policy from near-zero to over 90% on reasoning-limited tasks. We call the difference between what frozen motor skills achieve alone and inside the agentic loop the orchestration gap.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.21725 [cs.RO]
(or arXiv:2607.21725v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.21725
arXiv-issued DOI via DataCite
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From: Liane Galanti [view email] [v1] Thu, 23 Jul 2026 18:18:32 UTC (14,408 KB)
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