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Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation

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arxiv 2412.08468 v3 pith:BDTPKHEW submitted 2024-12-11 cs.RO cs.CV

Haosheng Li , Weixin Mao , Weipeng Deng , Chenyu Meng , Haoqiang Fan , Tiancai Wang , Yoshie Osamu , Ping Tan
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Hongan Wang Xiaoming Deng
This is my paper
classification cs.ROcs.CV
keywords graspmulti-graspllmroboticgenerationhandsmulti-handsemanticunified
verification ladder T0 review T1 audit T2 compute T3 formal
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Multi-hand semantic grasp generation aims to generate feasible and semantically appropriate grasp poses for different robotic hands based on natural language instructions. Although the task is highly valuable, due to the lack of multihand grasp datasets with fine-grained contact description between robotic hands and objects, it is still a long-standing difficult task. In this paper, we present Multi-GraspSet, the first large-scale multi-hand grasp dataset with automatically contact annotations. Based on Multi-GraspSet, we propose Multi-GraspLLM, a unified language-guided grasp generation framework, which leverages large language models (LLM) to handle variable-length sequences, generating grasp poses for diverse robotic hands in a single unified architecture. Multi-GraspLLM first aligns the encoded point cloud features and text features into a unified semantic space. It then generates grasp bin tokens that are subsequently converted into grasp pose for each robotic hand via hand-aware linear mapping. The experimental results demonstrate that our approach significantly outperforms existing methods in both real-world experiments and simulator. More information can be found on our project page https://multi-graspllm.github.io.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Hand-Object Interaction in the Age of Large Foundation Models:Reconstruction, Generation, and Embodied Transfer

    cs.CV 2026-07 conditional novelty 7.0 of 10

    A taxonomy of eight foundation-model priors organizes HOI reconstruction, generation, and embodied transfer, mapping what knowledge large models inject and where.

  2. R2HandoverSim: A Simulation Framework and Benchmark for Robot-to-Human Object Handovers

    cs.RO 2026-06 unverdicted novelty 6.0 of 10

    R2HandoverSim provides a reproducible simulation benchmark for robot-to-human handovers, showing that five complementary metrics correlate better with user-perceived quality than success rate alone.

  3. SECOND-Grasp: Semantic Contact-guided Dexterous Grasping

    cs.RO 2026-05 conditional novelty 6.0 of 10

    SECOND-Grasp integrates semantic contact proposals from vision-language reasoning with geometric refinement to achieve 98%+ lifting success and improved intent-aware grasping on seen and unseen objects.

  4. DextER: Language-driven Dexterous Grasp Generation with Embodied Reasoning

    cs.RO 2026-01 unverdicted novelty 6.0 of 10

    DextER uses contact-based embodied reasoning via autoregressive token generation to produce language-driven dexterous grasps, reaching 67.14% success on DexGYS with a 3.83 p.p. gain over prior methods and 96.4% better...

  5. Hand-Object Interaction in the Age of Large Foundation Models:Reconstruction, Generation, and Embodied Transfer

    cs.CV 2026-07 accept novelty 5.0 of 10

    Foundation-model HOI work is organized into eight geometric, semantic, and visual sub-priors that enter six reconstruction/generation tasks and three robot-transfer routes.

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