外观
论文arXiv 2608.257982 个月前
TacForcing：利用执行时触觉反馈的流式动作生成
原文链接：TacForcing: Streaming Action Generation with Execution-Time Tactile Feedback（arXiv 2608.25798 · 发布日期：2026-08-26）
TacForcing: Streaming Action Generation with Execution-Time Tactile Feedback
TacForcing面向接触丰富操作中触觉反馈滞后的问题，通过流式动作生成与EATA，在仿真和真实任务上验证。
核心要点
- 接触丰富操作的接触状态会在动作时域内变化，分块式VLA模型执行时触觉条件易过时。
- TacForcing用流式专家顺序生成动作块，并在执行后结合新触觉反馈从中间状态继续生成。
- EATA限制每次触觉更新仅直接作用于下一个将执行的动作块，以对齐触觉条件与执行。
- 在六个UniVTAC仿真任务和六个真实接触丰富操作任务上，平均成功率分别达65%和66%。
验证范围与局限：验证范围为六个UniVTAC仿真任务，以及两种机器人平台上的六个真实接触丰富操作任务。
英文摘要 (Abstract)
Contact-rich manipulation requires adapting to contact states that can evolve substantially within an action horizon. However, chunk-based vision-language-action models predict complete action chunks from observations collected before execution, leaving tactile conditioning stale during execution. Existing tactile-reactive approaches typically rely on separate high-frequency controllers, which increase both architectural and training complexity. In this paper, we introduce TacForcing, a streaming action-generation framework incorporating execution-time tactile feedback. TacForcing replaces the standard action expert with a streaming expert that generates action blocks sequentially while preserving intermediate states of unfinished blocks. After each block is executed, the expert resumes generation from these states using newly acquired tactile feedback. To better align tactile conditioning with action execution, we further introduce Execution-Aware Tactile Attention (EATA), which restricts direct access to each tactile update to the next block scheduled for execution. Across six UniVTAC simulation tasks and six real-world contact-rich manipulation tasks on two robot platforms, TacForcing achieves average success rates of 65% in simulation and 66% in the real world, outperforming the strongest baselines by 6 and 15 percentage points, respectively.
作者：Jianbo Zhou, Boyuan Zhao, Yuzheng Zhang, Yiyang Chen, Wenxin Chen, Qiuyue Li, Xiangyang Gu, Yuhan Cao, Xiao Xia, Yi Yang
来源
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