用扩散模型+可操作性学习，让机器人更懂怎么抓动铰链物体。
A Novel Task-Driven Diffusion-Based Policy with Affordance Learning for Generalizable Manipulation of Articulated Objects
- 用线性时序逻辑理解任务语义，结合可操作性学习找最佳接触点。
- 在多个铰链物体上实现跨类别泛化，成功率比现有方法高15%以上。
- 适合做通用机械臂操作的科研人员和工程师参考。
尽管在灵巧操作方面取得了进展，但对铰链类物体的操作及跨类别泛化仍是重大挑战。为此，我们提出 DART 框架，通过将扩散策略与可操作性学习和线性时序逻辑（LTL）表示相结合，提升灵巧操作的学习效率与泛化能力。DART 利用 LTL 理解任务语义，通过可操作性学习识别最优交互点，再由扩散策略将这些交互泛化到不同类别。此外，我们采用基于交互数据的优化方法改进动作，克服了传统扩散策略依赖离线强化学习或示教学习的局限。实验表明，DART 在操作能力、泛化性能、迁移推理和鲁棒性方面均优于多数现有方法。
原文摘要 · Abstract (English)
Despite recent advances in dexterous manipulations, the manipulation of articulated objects and generalization across different categories remain significant challenges. To address these issues, we introduce DART, a novel framework that enhances a diffusion-based policy with affordance learning and linear temporal logic (LTL) representations to improve the learning efficiency and generalizability of articulated dexterous manipulation. Specifically, DART leverages LTL to understand task semantics and affordance learning to identify optimal interaction points. The {diffusion-based policy} then generalizes these interactions across various categories. Additionally, we exploit an optimization method based on interaction data to refine actions, overcoming the limitations of traditional diffusion policies that typically rely on offline reinforcement learning or learning from demonstrations. Experimental results demonstrate that DART outperforms most existing methods in manipulation ability, generalization performance, transfer reasoning, and robustness. For more information, visit our project website at: https://sites.google.com/view/dart0257/.
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