外观
论文arXiv 2610.2249613 天前
机器人智能体在线探索的规模化
原文链接：Scaling Online Exploration for Robot Agents（arXiv 2610.22496 · 发布日期：2026-10-15）
Scaling Online Exploration for Robot Agents
RAX通过在线探索让机器人智能体生成可执行程序，在仿真与真机多形态操作任务上验证其泛化和自改进能力。
核心要点
- 新形态或新任务通常需专门遥操作数据，模仿策略泛化与持续改进受限。
- RAX由通用代码智能体生成控制代码，无需动作示范、手工动作原语或价值函数指导。
- 在线探索发现达成目标的决策序列，并编译为覆盖感知、规划、控制的模块程序。
- 在仿真平均较顶级基线模型高37.4%，真机可组合既有技能持续改进；RAX已开源。
验证范围与局限：验证范围为仿真与真机上的单臂、双臂、灵巧、移动和人形系统的精密及长时程操作任务。
英文摘要 (Abstract)
Robot learning has advanced by scaling demonstration datasets and model capacity; however, deploying a new robot embodiment or task typically necessitates a dedicated teleoperation corpus. The resulting policies remain bounded by their imitation distributions, limiting generalization and continual self-improvement. We introduce RAX (Robot Agent eXploration), an open-source framework for robot agents centered on scaling exploration: enabling agents to improve by acquiring online experience and synthesizing executable programs. In RAX, a general-purpose coding agent generates robot control code, operating without action demonstration data, hand-engineered action primitives, or value-function guidance. Through online exploration, the agent identifies decision sequences that achieve task goals and compiles them into modular programs encapsulating the full perception-planning-control loop. Using RAX, we present a systematic multi-embodiment study on scaling online exploration across precision and long-horizon manipulation tasks using single-arm, bimanual, dexterous, mobile, and humanoid systems in simulation and on real robots. Generalization performance on unseen environments improves consistently as exploration scales. In simulation, RAX outperforms top baseline models by +37.4% on average. On real robots, RAX autonomously learns memory-dependent, articulated, high-precision, deformable, and long-horizon mobile manipulation tasks without human demonstrations, continually improving performance by composing verified skills from prior exploration sessions. As exploration scales, we observe emergent capabilities including active perception, error recovery, safety guardrails, and human-robot collaboration. We open-source RAX as a generalist robot-agent platform in which new robot embodiments are integrated through their APIs alone, without teleoperated data collection or policy training.
作者：Runhan Huang, Xiangcheng Zhang, Haoyu Chen, Yitong Li, Yen‐Jen Wang, Bohan Lyu, Haotian Ye, Chuye Hong, Yixuan Wang, Chloe Su
来源
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