TactAlign：透過觸覺對準實現從人類到機器人的策略遷移

摘要

透過可穿戴裝置（如觸覺手套）收集的人類示範數據，為策略學習提供了快速靈活的監督指導，這些數據源自豐富自然的觸覺反饋。然而，核心挑戰在於如何將人類收集的觸覺信號遷移至機器人，儘管存在傳感模式與具身形態的差異。現有的人機傳遞方法在整合觸覺時，通常假設觸覺傳感器完全一致、需要配對數據，且幾乎不考慮人類示範者與機器人之間的具身差異，這限制了方法的可擴展性與通用性。我們提出TactAlign——一種跨具身觸覺對齊方法，可將人類收集的觸覺信號遷移至不同具身形態的機器人。該方法通過整流流技術，在無需配對數據集、人工標註或特權信息的情況下，將人類與機器人的觸覺觀測轉換為共享潛在表徵。我們的技術基於手物互動生成的偽配對數據，實現低成本潛在空間遷移。實驗表明，TactAlign在多個高接觸密度任務（旋轉、插入、蓋合）中提升人機策略傳遞效果，僅需不足5分鐘的人類數據即可泛化至未見物體與任務，並能實現高靈巧度任務（燈泡旋緊）的零樣本人機傳遞。

English

Human demonstrations collected by wearable devices (e.g., tactile gloves) provide fast and dexterous supervision for policy learning, and are guided by rich, natural tactile feedback. However, a key challenge is how to transfer human-collected tactile signals to robots despite the differences in sensing modalities and embodiment. Existing human-to-robot (H2R) approaches that incorporate touch often assume identical tactile sensors, require paired data, and involve little to no embodiment gap between human demonstrator and the robots, limiting scalability and generality. We propose TactAlign, a cross-embodiment tactile alignment method that transfers human-collected tactile signals to a robot with different embodiment. TactAlign transforms human and robot tactile observations into a shared latent representation using a rectified flow, without paired datasets, manual labels, or privileged information. Our method enables low-cost latent transport guided by hand-object interaction-derived pseudo-pairs. We demonstrate that TactAlign improves H2R policy transfer across multiple contact-rich tasks (pivoting, insertion, lid closing), generalizes to unseen objects and tasks with human data (less than 5 minutes), and enables zero-shot H2R transfer on a highly dexterous tasks (light bulb screwing).

TactAlign：透過觸覺對準實現從人類到機器人的策略遷移

TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment

摘要

Support