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.
Multi-graspllm: A multi- modal llm for multi-hand semantic guided grasp generation
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
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.
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 intention alignment.
citing papers explorer
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R2HandoverSim: A Simulation Framework and Benchmark for Robot-to-Human Object Handovers
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.
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SECOND-Grasp: Semantic Contact-guided Dexterous Grasping
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.
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DextER: Language-driven Dexterous Grasp Generation with Embodied Reasoning
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 intention alignment.