Rl Manipulation

Build the foundation of RL for manipulation — from MDP formulation and reward function design to setting up MuJoCo environments from scratch.

Train robots to grasp diverse objects with RL — from grasping reward design to curriculum learning with PPO and SAC.

Train a robot to carry a cup of water without spilling using RL — reward design for force control, impedance baseline, and SAC training.

Train precise pick-and-place with RL — HER for sparse rewards, 6-DOF placement, and orientation alignment with sub-cm accuracy.

Train robots to carry objects stably with RL — from carry-flat to pouring, multi-objective reward balancing speed vs stability.

Solve contact-rich manipulation with RL — peg-in-hole, assembly, tactile sensing, and domain randomization for sim-to-real transfer.

Teach robots to use tools with RL — affordance learning, two-phase grasp-then-use, and screwdriver insertion in MuJoCo.

Solving long-horizon manipulation with RL — curriculum learning, hierarchical RL, skill chaining, and IKEA furniture assembly benchmarks.