外观
论文arXiv 2608.187012 个月前
SoftVTBench：面向可变形物体操作的变形感知视触觉数据集与基准
原文链接：SoftVTBench: A Deformation-Aware Visuo-Tactile Dataset and Benchmark for Deformable-Object Manipulation（arXiv 2608.18701 · 发布日期：2026-08-19）
SoftVTBench: A Deformation-Aware Visuo-Tactile Dataset and Benchmark for Deformable-Object Manipulation
SoftVTBench用视触觉数据集和DSR闭环基准，在多种策略与分布转移设置上评估可变形物体操作的交互质量。
核心要点
- 现有基准多只看任务成功，可能忽略滑移或过度压缩等交互质量问题。
- 数据集含4000条专家演示和50余资产，同步RGB、触觉、动作及FEM状态。
- 基准以对象特定校准定义DSR，需完成任务且峰值归一化变形在容差内。
- 实验显示，同分布下三类策略均有成功rollout违反变形容差；分布转移下触觉更有益。
验证范围与局限：验证范围包括Diffusion Policy、π_{0.5}和FastWAM的12个同分布配置，以及分布转移下六组policy--suite比较。
英文摘要 (Abstract)
Physical interaction quality is central to deformable-object manipulation, yet most benchmarks evaluate task success alone. A policy may complete the task while allowing slip or causing excessive compression. A primary bottleneck is the absence of visuo-tactile datasets that pair policy-visible contact observations with independent physical ground truth over complete tasks. We introduce SoftVTBench, a visuo-tactile dataset for physical-interaction-aware deformable-object manipulation. It contains 4,000 expert demonstrations and more than 50 assets, including volumetric deformable objects and visually matched rigid twins. At 20 Hz, each episode synchronizes multi-view RGB, dual-finger tactile RGB and marker motion, proprioception, language, and binary and continuous gripper actions, alongside evaluator-only finite-element (FEM) states. Building upon this dataset, we establish a closed-loop benchmark that uses fixed object-specific calibration to define the Deformation-aware Success Rate (DSR), which counts a rollout as successful only when it completes the task and keeps peak normalized deformation within tolerance. Across Diffusion Policy, $π_{0.5}$, and FastWAM, all 12 in-distribution configurations contain successful rollouts that violate the deformation tolerance, accounting for 0.7--24% of each configuration's successes. Under distribution shift, visuo-tactile variants achieve higher task success in all six policy--suite comparisons and higher DSR in five, whereas their in-distribution benefits are mixed. These results show that making touch available does not by itself ensure effective multimodal fusion. SoftVTBench therefore provides a common visuo-tactile resource for studying not only whether a policy succeeds, but how it physically interacts with deformable objects and when touch improves that interaction.
作者：Bowen Jing, Mingxin Wang, Ruiyang Hao, Chenchen Ge, Hanwen Shen, Junjie He, Yang Cui, Yiming Hou, Weitao Zhou, Jiawei Wang
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