散らかった環境で言語指示により物体を掴むVLMエージェント型ロボットオーケストレータCLARO
CLARO: Clutter Aware Robot Orchestrator for Open-Vocabulary Grasping using VLM Agents
VLMエージェントが物体検出・セグメンテーション・6自由度把持・押し動作を統合し、散らかった環境で言語指定の対象物を探索・把持するシステムを構築、シミュレーションと実機で高い成功率を示した。
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著者: Sungsu Kim, Vineet Bhat, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami
分類: cs.RO, cs.CV
原文アブストラクト
Robots that follow natural-language instructions unlock a wide range of autonomous applications in households, warehouses, and labs. Real-world environments are diverse and often heavily cluttered, requiring robots to plan over multiple steps, act, and replan as they work toward a goal. We present CLARO (CLutter-Aware Robot Orchestrator), a VLM agent that orchestrates a set of perception and manipulation skills: open-vocabulary object detection, segmentation, 6-DoF grasping, and non-prehensile pushing to locate and retrieve a language-specified target in clutter. CLARO maintains a memory of its past actions to support long-horizon execution, and emits manipulation targets (grasp poses/3D waypoints) allowing transfer across robot arms without task-specific training. We evaluate CLARO against a range of methods on a dynamic simulation environment and standard image-based benchmarks, demonstrating strong generalization across object classes, lighting, and clutter levels. CLARO attains an average success rate of 89.1%, a relative improvement of over 16% on the strongest competing method in high cluttered scenes. We further analyze performance along grounding and grasping axes to characterize where each method succeeds and fails. Finally, we deploy CLARO on a robot arm across diverse cluttered scenes, achieving a success rate of 88.8%, and demonstrating strong sim2real generalizability.
