外观
论文arXiv 2609.298221 个月前
用于机器人模仿学习的指尖感知到本体感知与主动动作的自监督锚定
原文链接：Self-Supervised Anchoring of Fingertip Sensing to Proprioception and Proactive Actions for Robot Imitation Learning（arXiv 2609.29822 · 发布日期：2026-09-24）
Self-Supervised Anchoring of Fingertip Sensing to Proprioception and Proactive Actions for Robot Imitation Learning
研究将指尖触觉与接近传感融入模仿学习，提出PROPRA预训练，并在真实操作任务上验证提升成功率。
核心要点
- 外部相机难以观察指尖附近的物体接近、接触开始与抓取状态。
- PROPRA将各指尖传感历史分别与本体感知和动作片段对齐。
- 真实操作实验中，该方法优于纯视觉策略和图像锚定预训练基线。
- 项目页面已提供：https://tomohiromotoda.github.io/nia.propra/
验证范围与局限：验证范围为真实世界操作任务，摘要未说明具体任务数量或机器人配置。
英文摘要 (Abstract)
Robotic imitation learning often relies on external cameras, yet local interaction cues such as object proximity, contact onset, and grasp state are difficult to observe near the fingertips because of occlusion and limited temporal resolution. We study how to effectively incorporate complementary fingertip sensing into imitation learning using pressure-sensitive tactile and reflective proximity sensors, along with pretrained sensor encoders. The two modalities provide information at different manipulation phases: proximity sensing is informative before contact, whereas tactile sensing becomes informative after contact. However, naively adding these signals to a policy does not consistently improve performance and can even underperform vision-only policies, suggesting that sparse, phase-dependent sensor signals are difficult to exploit from limited demonstrations. We therefore propose a proprioception-anchored pretraining method, PROprioceptive-and-PRoactive Anchoring (PROPRA), which independently aligns each fingertip sensor history with proprioceptive and action segments. This provides a continuously available sensorimotor reference, allowing each sensor to be aligned independently during its informative phases. Experiments on real-world manipulation tasks show that our pretraining method improves average success rates over vision-only policies and image-anchored pretraining baselines. Representation analysis further shows that it preserves richer information about pre-contact states, enabling more effective use of complementary fingertip sensing. Please refer to our project page: https://tomohiromotoda.github.io/nia.propra/
作者：Tomohiro Motoda, Masaki Murooka, Keisuke Shirai, Hanbit Oh, Ryoichi Nakajo, Shotaro Miwa, Roman Mykhailyshyn, Hugo Duarte, Yukiyasu Domae
来源
本页事实对应的公开页面，以原文为准。