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Robotic framework GSAM enhances articulated object manipulation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GSAM, a new robotic framework designed to improve the manipulation of articulated objects. This system uses a vision-based perceiver and a fine-tuned VLM with chain-of-thought reasoning to refine object perception. GSAM also incorporates an interaction constraint function generator and an LLM for trajectory planning, aiming to prevent collisions and enhance generalization across various object types and interaction scenarios. AI

IMPACT This framework could improve the dexterity and safety of robots in complex manipulation tasks, potentially accelerating their deployment in service industries.

RANK_REASON The cluster contains an academic paper detailing a new framework for robotic manipulation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

Robotic framework GSAM enhances articulated object manipulation

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The cluster contains an academic paper detailing a new framework for robotic manipulation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Beichen Shao, Mengying Xie, Heng Su, Wanyi Zhang, Mingyan Li, Yan Ding, Fausto Giunchiglia, Chao Chen ·

    GSAM: A Generalizable and Safe Robotic Framework for Articulated Object Manipulation

    arXiv:2605.30740v1 Announce Type: cross Abstract: Articulated object manipulation is a unique challenge for service robots. Existing methods employ end-to-end policy learning, visionmotion planning, and large-language/visual-language model (LLM/VLM), but often overlook the divers…

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