混雑環境での大規模クロスエンボディメント巧みな把持
T(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at Scale
8種の巧みなハンドと約5万物体にわたる約5億の把持アノテーションを備えた大規模シミュレーションベンチマークを構築し、混雑環境でのクロスエンボディメント把持合成の拡散モデルT(R,O) Grasp++を提案した。平均成功率88%、推論遅延0.3秒を達成。
著者: Xin Fei, Zhixuan Xu, Huaicong Fang, Fangjun Chen, Zhaoyu Mu, Rui Yang, Zhexi Luo, Lin Shao
分類: cs.RO
原文アブストラクト
Dexterous grasping in cluttered scenes remains a central challenge in robotic manipulation, as it requires high-dimensional hand coordination under complex object-environment constraints. To address this problem, we introduce one of the largest simulation benchmarks to date, comprising around 0.5B grasp annotations across eight dexterous hands and approximately 50K objects. Furthermore, we propose T(R,O) Grasp++, a scalable diffusion-based framework for cross-embodiment dexterous grasp synthesis in cluttered environments. Extensive experiments demonstrate that our model significantly outperforms existing baselines, achieving an average success rate of 88% across eight dexterous embodiments. Meanwhile, our pipeline achieves only 0.3s inference latency while improving inference sample efficiency by over 10 times.