论文标题
dext-gen：在稀疏的奖励环境中巧妙的抓握，完全取向控制
Dext-Gen: Dexterous Grasping in Sparse Reward Environments with Full Orientation Control
论文作者
论文摘要
强化学习是机器人抓握的一种有前途的方法，因为它可以在困难的情况下学习有效的掌握和掌握政策。但是，由于问题的高维度，用精致的机器人手来实现类似人类的操纵能力是具有挑战性的。尽管可以采用奖励成型或专家示范等补救措施来克服这个问题，但它们通常会导致过分简化和有偏见的政策。我们提出了Dext-Gen，这是一种在稀疏奖励环境中灵巧抓握的增强学习框架，适用于各种抓手，并学习无偏见和复杂的政策。通过平滑方向表示实现了抓地力和物体的全向控制。我们的方法具有合理的培训时间，并提供了包括所需先验知识的选项。模拟实验证明了框架对不同情况的有效性和适应性。
Reinforcement learning is a promising method for robotic grasping as it can learn effective reaching and grasping policies in difficult scenarios. However, achieving human-like manipulation capabilities with sophisticated robotic hands is challenging because of the problem's high dimensionality. Although remedies such as reward shaping or expert demonstrations can be employed to overcome this issue, they often lead to oversimplified and biased policies. We present Dext-Gen, a reinforcement learning framework for Dexterous Grasping in sparse reward ENvironments that is applicable to a variety of grippers and learns unbiased and intricate policies. Full orientation control of the gripper and object is achieved through smooth orientation representation. Our approach has reasonable training durations and provides the option to include desired prior knowledge. The effectiveness and adaptability of the framework to different scenarios is demonstrated in simulated experiments.
