T-Rex: 触觉反应灵巧操作
T-Rex: Tactile-Reactive Dexterous Manipulation
AI总结：
提出大规模触觉数据集和可变速率混合Transformer架构，在12项精细操作任务上平均成功率提升超30%。
摘要：
长期以来，对触觉信号做出动态反应的能力被认为是实现敏捷人类级灵巧操作的关键。然而，当前基于学习的视觉-语言-动作（VLA）模型在机器人操作中通常要么忽略触觉模态，要么局限于使用静态线索的编码器，部分原因是缺乏多样化的训练数据和标准化评估、当前VLA模型中的架构限制以及静态触觉编码器的局限性。在本文中，我们通过解决所有这些局限性来推动触觉反应操作的前沿。我们提出了一个大规模、100小时的触觉丰富数据集，该数据集通过一种新颖的、数据高效的配方收集，优先考虑基本运动基元。为了有效利用自然高频的触觉信号而不牺牲现有VLA的现有能力，我们引入了一种可变速率混合Transformer（MoT）架构，配备了一种新颖的时间触觉VQ-VAE编码器。我们在12项需要精细力控制和可变形物体操作的操作任务上展示了触觉反应策略的有效性，平均成功率比最强基线高出30%以上。
英文摘要：
The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) models for robotic manipulation generally either overlook the tactile modality or are limited to encoders with static cues, due in part to the scarcity of diverse training data and standardized evaluation, architectural constraints in current VLA models, and limitations of static tactile encoders. In this paper, we push the frontier of tactile-reactive manipulation by addressing all of these limitations. We propose a large-scale, 100-hour tactile-rich dataset collected via a novel, data-efficient recipe that prioritizes elementary motor primitives. To effectively exploit naturally high-frequency touch signals without sacrificing the existing capabilities of existing VLAs, we introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder. We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation, achieving over 30% higher average success rate than the strongest baseline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
