发表机构
Princeton University; Together AI（普林斯顿大学; 联合人工智能）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究通用机器人行动推理问题，提出将相关能力分解为语言条件策略/控制智能体与高级智能体管理器/编排器，构建物理智能体编排器Pigey，经评估其在模拟和实际任务中性能显著提升，缩小了编排差距。
AI 中文摘要
通用机器人需要综合感知、世界知识、规划、成功检测、恢复和低级控制来对其行动进行推理。当前的先进模型试图通过大规模预训练将所有这些能力整合到学习到的策略中。本文表明这些能力可分解为通用语言条件策略/控制智能体和高级智能体管理器/编排器。构建了一个闭环物理智能体编排器，能进行高级规划、分解目标、指挥低级电机命令、跟踪和验证结果并从失败中恢复。通过模拟基准和实际机器人操作任务评估，该编排器性能显著提升，缩小了编排差距。
英文摘要
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.
