On my RL-Based Robotic Pick-and-Place project, I’m developing an end-to-end reinforcement learning system for autonomous pick-and-place using a 5-DOF robotic arm.
I created a custom PyBullet environment with the arm, workspace, objects, target positions, physics, and collision configuration. I also designed its observation and continuous-action spaces, reset, and step-transition mechanisms.
I’m optimizing a TD3 agent and integrated Hindsight Experience Replay to improve learning from unsuccessful and alternative-goal experiences. The training pipeline includes experience replay, HER transition relabeling, actor-critic updates, target network updates, and exploration noise.
I designed the reward function around task-related distance and manipulation states, and implemented training and evaluation workflows to monitor behavior and identify failure cases. I also developed a custom URDF/Xacro model of the arm for ROS 2 and simulation.
