robotic manipulation with RL

Manipulation means teaching a robot arm and gripper to grasp, push, stack, insert, or reorient objects — tasks a toddler finds easy and a robot finds brutally hard, because contact is unpredictable and the space of hand and object configurations is enormous.

The state might be joint angles plus camera images; the actions are joint torques or end-effector motions; the reward encodes task success, such as an object lifted or a peg inserted. RL is attractive because it can discover contact-rich skills that are painful to hand-program. But real-robot data is scarce, so most work trains in simulation with domain randomization, bootstraps from human demonstrations via imitation learning, or learns offline from logged interactions.

Reward design is delicate here. A sparse success-only reward is honest but very hard to learn from, while a dense shaped reward is easy to game — the gripper learns to hover near the object for partial credit instead of actually grasping it.