RobotUse: Allocating Computation, Context, and Decisions

Hugging Face Daily Papers Papers
robotics embodied-ai agent-harness motion-planning task-execution

Summary

RobotUse 引入一种围绕指定与修订物理动作来组织计算、上下文与决策的机器人智能体 harness，使智能体通过视觉选择目标与位姿、后端处理几何与运动规划，并在执行中持续学习，最终在 RoboLab 上取得 45% 的任务成功率，超过 CaP-X 6.7 个百分点。

Robot agents must connect their intended actions to observed outcomes while retaining the context needed to revise their choices over repeated attempts. Existing interfaces often leave these choices inside predefined tools or require agents to manage detailed execution code and its growing history. We introduce RobotUse, a robot agent harness that organizes computation, context, and decisions around specifying and revising physical actions. Agents visually select targets and poses, while the backend handles geometry, motion planning, and control. Subagents retain detailed interactions within each subgoal and return the information needed for subsequent decisions. Continual harnessing lets agents learn from execution by updating a persistent playbook. On RoboLab, RobotUse achieves 45% task success, outperforming CaP-X by 6.7 percentage points while maintaining compact decision contexts and reducing reliance on predefined action abstractions. Furthermore, we show that RobotUse learns from real-world execution despite imperfect feedback and transfers what it learns to subsequent tasks. Project page is available at https://robotuse-team.github.io/.
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Source: https://huggingface.co/papers/2610.04929 Published on Oct 4

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Submitted byhttps://huggingface.co/Leejh123e

Junhooon Oct 6

Abstract

Robotagentsmustconnecttheirintendedactionstoobservedoutcomeswhileretainingthecontextneededtorevisetheirchoicesoverrepeatedattempts.Existinginterfacesoftenleavethesechoicesinsidepredefinedtoolsorrequireagentstomanagedetailedexecutioncodeanditsgrowinghistory.WeintroduceRobotUse,arobotagentharnessthatorganizescomputation,context,anddecisionsaroundspecifyingandrevisingphysicalactions.Agentsvisuallyselecttargetsandposes,whilethebackendhandlesgeometry,motionplanning,andcontrol.Subagentsretaindetailedinteractionswithineachsubgoalandreturntheinformationneededforsubsequentdecisions.Continualharnessingletsagentslearnfromexecutionbyupdatingapersistentplaybook.OnRoboLab,RobotUseachieves45%tasksuccess,outperformingCaP-Xby6.7percentagepointswhilemaintainingcompactdecisioncontextsandreducingrelianceonpredefinedactionabstractions.Furthermore,weshowthatRobotUselearnsfromreal-worldexecutiondespiteimperfectfeedbackandtransferswhatitlearnstosubsequenttasks.Projectpageisavailableathttps://robotuse-team.github.io/.

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