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Reinforcement learning is a promising method for robotic grasping as it canlearn effective reaching and grasping policies in difficult scenarios. However,achieving human-like manipulation capabilities with sophisticated robotic handsis challenging because of the problem's high dimensionality. Although remediessuch as reward shaping or expert demonstrations can be employed to overcomethis issue, they often lead to oversimplified and biased policies. We presentDext-Gen, a reinforcement learning framework for Dexterous Grasping in sparsereward ENvironments that is applicable to a variety of grippers and learnsunbiased and intricate policies. Full orientation control of the gripper andobject is achieved through smooth orientation representation. Our approach hasreasonable training durations and provides the option to include desired priorknowledge. The effectiveness and adaptability of the framework to differentscenarios is demonstrated in simulated experiments.

Dext-Gen: Dexterous Grasping in Sparse Reward Environments with Full Orientation Control