外观
论文arXiv 2609.309691 个月前
TACTIC：理解面向接触丰富机器人操作策略的触觉编码器与条件融合
原文链接：TACTIC: Understanding Tactile Encoders and Conditioning for Contact-rich Robot Manipulation Policies（arXiv 2609.30969 · 发布日期：2026-09-25）
TACTIC: Understanding Tactile Encoders and Conditioning for Contact-rich Robot Manipulation Policies
研究触觉编码器与融合策略对接触丰富操作策略的影响，并在真实实验中统一评估验证。
核心要点
- 视觉式触觉传感器便于端到端策略设计，但架构、数据和评测差异使设计选择难以判断。
- TACTIC在统一训练与实验流程下，系统比较触觉编码器和视觉-触觉融合策略。
- 研究通过超过2000次真实世界rollout，在多种接触丰富操作任务上评估所有模型。
- 结果表明不存在通用最优表征或融合方案，最佳编码骨干和融合方式强依赖任务。
验证范围与局限：验证范围为真实世界中的多种接触丰富操作任务和超过2000次rollout。
英文摘要 (Abstract)
Tactile information is essential for contact-rich manipulation tasks in robotics. Vision-based tactile sensors make it particularly easy to design end-to-end manipulation policies with tactile sensing, as they enable the use of existing encoders from computer vision. However, this has led to a huge variety of architectures, training datasets, and evaluation protocols, making it difficult to determine which design choices best encode touch. In this work, we address this gap and present a comprehensive study of tactile encoders and fusion strategies across various contact-rich manipulation tasks in real-world experiments. To enable a controlled comparison, we train and evaluate all models under the same pipeline and experimental setup, comprising more than 2000 real-world rollouts. Our results go beyond other studies that only compare simulation performance, which does not necessarily translate to real-world settings, where large-scale evaluations are needed to obtain reliable statistics. Our key finding is that there is no universally optimal representation or fusion strategy for encoding visual-tactile. Instead, the best encoder backbone and fusion scheme depend strongly on the task.
作者：Seongjin Bien, Débora Oliveira Makowski, Carlo Kneissl, Reihaneh Mirjalili, Pankhuri Vanjani, Rudolf Lioutikov, Gitta Kutyniok, Florian Walter, Wolfram Burgard
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