用强化学习的评价网络选好抓取姿势，让机械手自动翻转物体。
Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic
- 用已训练好的操纵模型的评价网络评分并挑选初始抓取姿势。
- 真实机器人实验中操纵成功率显著提升，无需额外训练。
- 适合想实现全自动抓取与翻转的机器人研究者。
手部操作与抓取是机器人领域的基础任务，但通常被分开处理。尽管强化学习在生成手部操作策略方面取得成功，但实际应用仍受限于需人工将物体置于合适初始抓取状态。如何找到既稳定又能促进目标操作的初始抓取仍是开放问题。本文提出一种方法：利用为手部操作训练的强化学习代理的评价网络，对初始抓取进行评分并选择最优方案。实验表明，该方法显著提升手部操作成功率，且无需额外训练。我们还实现在真实系统上的完整抓取-操作流水线，实现了对不规则物体的自主抓取与重定向。
原文摘要 · Abstract (English)
In-hand manipulation and grasping are fundamental yet often separately addressed tasks in robotics. For deriving in-hand manipulation policies, reinforcement learning has recently shown great success. However, the derived controllers are not yet useful in real-world scenarios because they often require a human operator to place the objects in suitable initial (grasping) states. Finding stable grasps that also promote the desired in-hand manipulation goal is an open problem. In this work, we propose a method for bridging this gap by leveraging the critic network of a reinforcement learning agent trained for in-hand manipulation to score and select initial grasps. Our experiments show that this method significantly increases the success rate of in-hand manipulation without requiring additional training. We also present an implementation of a full grasp manipulation pipeline on a real-world system, enabling autonomous grasping and reorientation even of unwieldy objects.
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