用优化生成演示，让机器人高效学会靠力觉和视觉翻转物体。
Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations
- 结合优化生成的示范与深度强化学习，实现样本高效训练。
- 在仿真中成功完成多种翻转任务，实机验证可仅凭感知反馈操作。
- 适合需灵活应对新物体的工业抓取场景，无需预先知道物体参数。
非握持式操作因物体、环境与机器人之间的复杂接触交互而具有挑战性。基于模型的方法能高效生成满足接触约束的复杂轨迹，但对模型误差敏感，且常需访问物体质量、尺寸、位姿等特权信息，难以适应新物体。相比之下，学习方法对建模误差更鲁棒，但通常需要大量数据。本文提出一种闭环翻转操作的学习框架，利用计算高效的接触隐式轨迹优化（CITO）生成示范，指导深度强化学习，实现样本高效学习。同时提出一种基于特权训练的仿真到现实迁移策略，使机器人仅依赖本体感知、视觉和力觉即可完成翻转，无需特权信息。方法在多个翻转任务上进行评估，证明其具备成功的仿真到现实迁移能力。
原文摘要 · Abstract (English)
Non-prehensile manipulation is challenging due to complex contact interactions between objects, the environment, and robots. Model-based approaches can efficiently generate complex trajectories of robots and objects under contact constraints. However, they tend to be sensitive to model inaccuracies and require access to privileged information (e.g., object mass, size, pose), making them less suitable for novel objects. In contrast, learning-based approaches are typically more robust to modeling errors but require large amounts of data. In this paper, we bridge these two approaches to propose a framework for learning closed-loop pivoting manipulation. By leveraging computationally efficient Contact-Implicit Trajectory Optimization (CITO), we design demonstration-guided deep Reinforcement Learning (RL), leading to sample-efficient learning. We also present a sim-to-real transfer approach using a privileged training strategy, enabling the robot to perform pivoting manipulation using only proprioception, vision, and force sensing without access to privileged information. Our method is evaluated on several pivoting tasks, demonstrating that it can successfully perform sim-to-real transfer. The overview of our method and the hardware experiments are shown at https://youtu.be/akjGDgfwLbM?si=QVw6ExoPy2VsU2g6
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