外观
论文arXiv 2609.157261 个月前
Bench2Dex：跨灵巧手的视觉-触觉双手灵巧操作基准测试
原文链接：Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands（arXiv 2609.15726 · 发布日期：2026-09-14）
Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands
Bench2Dex用统一仿真触觉接口，在12种灵巧手的26个双手任务上评测视觉-触觉学习方法。
核心要点
- 触觉可提供视觉难以推断的接触信息，但灵巧手硬件和仿真触觉信号差异大，缺少一致实验环境。
- 该基准为现有机器人模型加入共享仿真触觉接口，将局部接触几何转为图像式触觉观测。
- Bench2Dex包含26个双手操作任务、约1.3K人类遥操作示范，并提供多模态观测和可执行指标。
- 论文评估ACT、Diffusion Policy、pi0.5和GR00T N1.5，报告性能与失败模式，并强调仿真触觉不替代真实传感。
验证范围与局限：验证范围为仿真中的12种灵巧手、26个双手操作任务及七类扰动；未声称替代真实触觉传感。
英文摘要 (Abstract)
Tactile sensing provides contact information that can be difficult to infer from vision alone, but tactile hardware for dexterous hands has not converged to a common design. Dexterous hands differ in finger structure, contact surfaces, and sensor layouts, while simulated tactile signals still differ from measurements produced by physical sensors. These factors make it difficult to study visuo-tactile manipulation across diverse dexterous hands within a consistent experimental setting. We present Bench2Dex, a simulation benchmark for visuo-tactile bimanual manipulation across 12 dexterous hands. We adapt existing robot models with a shared simulated tactile interface that converts local contact geometry into image-like tactile observations. The interface provides a consistent observation format across different hand morphologies without attempting to reproduce the output of a specific physical tactile sensor. Bench2Dex includes 26 bimanual manipulation tasks that involve tool use, articulated-object interaction, and multi-stage manipulation, together with about 1.3K human-teleoperated demonstrations. The benchmark provides synchronized visual, tactile, proprioceptive, action, and object-state observations, together with executable task metrics. For robustness, we group seven perturbation types into invariance axis, where the correct action does not change, and equivariance axis, where the correct action changes together with the perturbation. We evaluate ACT, Diffusion Policy, pi0.5, and GR00T N1.5 on Bench2Dex and report their performance and failure modes. Bench2Dex is meant as a platform for studying visuo-tactile learning across dexterous hands. It does not assume that simulated tactile observations can replace real tactile sensing; it offers a shared setting for algorithm development while tactile hardware and simulation models are still evolving.
作者：Zhenjie Yang, Yideng Zhang, Dongjie Zhang, Chenyu Jiang, Xianshuai Liu, Yufeng Li, Zuhao Ge, Xingyu Jiao, Zheng Zhang, Kaiyu He
来源
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