  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.
