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New VLM Agent CLARO Masters Grasping in Cluttered Robot Environments

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CLARO, a vision-language model agent designed for robots to grasp objects in cluttered environments. CLARO uses open-vocabulary object detection and a memory of past actions to plan and execute multi-step retrieval tasks. It demonstrates strong generalization across various conditions and has achieved an 88.8% success rate in real-world robot arm deployments. AI

IMPACT Enhances robot capabilities for complex manipulation tasks in unstructured environments.

RANK_REASON Research paper published on arXiv detailing a new VLM agent for robotics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New VLM Agent CLARO Masters Grasping in Cluttered Robot Environments

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Research paper published on arXiv detailing a new VLM agent for robotics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Sungsu Kim, Vineet Bhat, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami ·

    CLARO: Clutter Aware Robot Orchestrator for Open-Vocabulary Grasping using VLM Agents

    arXiv:2610.24016v2 Announce Type: replace-cross Abstract: 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…

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