Towards Multi-Objective Object Push-Grasp Policy Based on Maximum Entropy Deep Reinforcement Learning under Sparse Rewards
In unstructured environments, robots need to deal with a wide variety of objects with diverse shapes, and often, the instances of these objects are unknown.Traditional methods rely on Display prom training with large-scale labeled data, but in environments with continuous and high-dimensional state spaces, the data become sparse, leading to weak ge