DexTrack：朝向從人類參考實現靈巧操作的通用神經跟蹤控制

DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References

February 13, 2025

作者: Xueyi Liu, Jianibieke Adalibieke, Qianwei Han, Yuzhe Qin, Li Yi

cs.AI

摘要

我們致力於開發一個通用的神經追踪控制器，用於從人類參考中進行靈巧操作。該控制器旨在管理靈巧機器人手，以便根據人體與物體之間的運動學互動所定義的各種目的來操作不同的物體。開發這樣的控制器受到靈巧操作的複雜接觸動力學以及對適應性、通用性和穩健性的需求的挑戰。由於當前的強化學習和軌跡優化方法往往依賴於特定任務的獎勵或精確系統模型，因此這變得複雜。我們提出了一種方法，通過精心挑選大規模成功的機器人追踪演示，包括人類參考和機器人動作對，來訓練一個神經控制器。利用數據滾輪，我們通過不斷增強控制器的性能以及成功追踪演示的數量和質量來進行迭代。我們利用可用的追踪演示，並精心整合強化學習和模仿學習，以提高控制器在動態環境中的性能。同時，為了獲得高質量的追踪演示，我們通過在同伦优化方法中利用學習的追踪控制器來個別優化每個軌跡的追踪。同伦优化，模仿思維鏈，有助於解決具有挑戰性的軌跡追踪問題，以增加演示的多樣性。我們通過訓練一個通用的神經控制器並在模擬和真實世界中進行評估來展示我們的成功。我們的方法相對於領先基準線實現了超過10%的成功率提升。項目網站上提供了帶有動畫結果的鏈接：https://meowuu7.github.io/DexTrack/。

English

We address the challenge of developing a generalizable neural tracking controller for dexterous manipulation from human references. This controller aims to manage a dexterous robot hand to manipulate diverse objects for various purposes defined by kinematic human-object interactions. Developing such a controller is complicated by the intricate contact dynamics of dexterous manipulation and the need for adaptivity, generalizability, and robustness. Current reinforcement learning and trajectory optimization methods often fall short due to their dependence on task-specific rewards or precise system models. We introduce an approach that curates large-scale successful robot tracking demonstrations, comprising pairs of human references and robot actions, to train a neural controller. Utilizing a data flywheel, we iteratively enhance the controller's performance, as well as the number and quality of successful tracking demonstrations. We exploit available tracking demonstrations and carefully integrate reinforcement learning and imitation learning to boost the controller's performance in dynamic environments. At the same time, to obtain high-quality tracking demonstrations, we individually optimize per-trajectory tracking by leveraging the learned tracking controller in a homotopy optimization method. The homotopy optimization, mimicking chain-of-thought, aids in solving challenging trajectory tracking problems to increase demonstration diversity. We showcase our success by training a generalizable neural controller and evaluating it in both simulation and real world. Our method achieves over a 10% improvement in success rates compared to leading baselines. The project website with animated results is available at https://meowuu7.github.io/DexTrack/.