用软工具一次性动态操控刚体，隐式学习物理属性预测动作。
Implicit Physics-aware Policy for Dynamic Manipulation of Rigid Objects via Soft Body Tools
- 通过隐式系统辨识自动获取未知物理参数
- 单次尝试即完成刚体远距离搬运，成功率显著优于基线
- 适合需要柔体工具动态交互的机器人操控场景
近期机器人工具使用研究拓展了新任务应用，但主要聚焦刚性工具，对柔性工具与刚体间动态交互的研究仍属空白。本文首次探索利用软体工具进行刚体的一次性动态操控，该问题因复杂相互作用和不可观测的物理属性而极具挑战。为此，我们提出隐式物理感知（IPA）策略，通过系统辨识隐式提取物理信息，并据此预测目标条件下的单次动作。我们在一个高难度任务中验证方法：在未知环境物理参数下，仅用一次尝试，借助绳索等软工具将刚体运送到远处目标位置。实验表明，该方法能高效识别物理属性、准确预测动作，并在真实环境中实现平滑泛化。相关视频见：https://youtu.be/4hPrUDTc4Rg?si=WUZrT2vjLMt8qRWA
原文摘要 · Abstract (English)
Recent advancements in robot tool use have unlocked their usage for novel tasks, yet the predominant focus is on rigid-body tools, while the investigation of soft-body tools and their dynamic interaction with rigid bodies remains unexplored. This paper takes a pioneering step towards dynamic one-shot soft tool use for manipulating rigid objects, a challenging problem posed by complex interactions and unobservable physical properties. To address these problems, we propose the Implicit Physics-aware (IPA) policy, designed to facilitate effective soft tool use across various environmental configurations. The IPA policy conducts system identification to implicitly identify physics information and predict goal-conditioned, one-shot actions accordingly. We validate our approach through a challenging task, i.e., transporting rigid objects using soft tools such as ropes to distant target positions in a single attempt under unknown environment physics parameters. Our experimental results indicate the effectiveness of our method in efficiently identifying physical properties, accurately predicting actions, and smoothly generalizing to real-world environments. The related video is available at: https://youtu.be/4hPrUDTc4Rg?si=WUZrT2vjLMt8qRWA
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