Generalist robot manipulation policies have developed rapidly, yet their reliable evaluation remains challenging due to fundamental flaws in existing simulation benchmarks: prominent sim-to-real gaps, narrow task coverage, and unfair evaluation caused by ambiguous training-test pipelines. Prior works only partially res...
Lian Ruan, Jade Yang, Sherphylan Gao et al.· 0 citations
Robotic manipulation of flat objects is challenging due to the ungraspable configurations and strong variations in object geometry and material. Existing methods rely on heuristic pre-manipulation and are often evaluated in closed settings with limited generalization. We propose a unified framework that decouples the m...
Xin-Hai Zhu, Wenshuo Han, Zhou Wang et al.· 0 citations
HeteroGenManip is proposed, a task-conditioned, two-stage framework designed to decouple initial grasp from complex interaction execution, and achieves robust intra-category shape and pose generalization.
From YAML-first specifications that decouple contents, placement, behavior, and agent exposure, MagicSim constructs diverse executable worlds spanning task families, interaction regimes, physics, layouts, sensors, avatars, and robot embodiments in one reset-and-step loop.
This work proposes a unified visuo-tactile-fusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive refinement and introduces an efficient visuo-tactile representation that tightly fuses object geometry with tactile feedback by associating tactile signals with finger identities.
Xi-Rui Liang, Jia-Qi Liang, Jing-Kai Xu et al.· 0 citations
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