Refining Robotic Manipulation

This chapter explores the complexities of contact point generation and trajectory planning for robotic tasks, focusing on the influence of marker placement and parameter tuning on performance. The discussion highlights the challenges of sim-to-real reinforcement learning and the importance of human-guided simulations to enhance efficiency in training robotic policies. Additionally, it delves into the limitations of dexterous manipulation and the potential of domain randomization to improve grasping capabilities.