Reinforcement Learning for Robotic Manipulation with MuJoCo
Applied reinforcement learning techniques to train a Franka Emika Panda robotic arm to autonomously grasp and lift a cube using the Proximal Policy Optimization (PPO) algorithm in a MuJoCo simulation environment.
Project Documentation
Download the complete project report with technical details, reward function design, PPO algorithm implementation, and training results.
View PDF ReportOverview
This project applied reinforcement learning techniques to train a Franka Emika Panda robotic arm to autonomously grasp and lift a cube using the Proximal Policy Optimization (PPO) algorithm in a MuJoCo simulation environment. The project focused on designing effective reward functions through iterative refinement, addressing challenges such as unintended behaviors where the robot would balance the cube on its vertices instead of properly lifting it. Implemented reward shaping with intermediate rewards to guide step-by-step progress, optimized hyperparameters for training stability, and developed enhanced observation metrics including cube height and grasp counts for improved debugging and observability.
Approaches
Reward Function Design
Designed and iteratively refined reward functions using reward shaping techniques. Implemented intermediate rewards for reducing distance between gripper and cube, successful grasping and lifting, and penalties for dropping the cube or twisting the arm. This approach enabled step-by-step progress guidance rather than relying solely on sparse rewards.
PPO Algorithm Implementation
Implemented Proximal Policy Optimization (PPO) algorithm, a widely used reinforcement learning method for continuous control tasks. The algorithm enabled stable policy learning through clipped objective functions and multiple epochs of policy updates per batch.
MuJoCo Simulation Environment
Leveraged MuJoCo physics engine for high-fidelity modeling of rigid-body dynamics and continuous control scenarios. The environment featured realistic physical properties including friction, gravity, and collision detection for accurate simulation of the Franka Emika Panda robotic arm.
Enhanced Observation and Metrics
Developed additional observation metrics including cube height and grasp counts to enhance observability and enable more efficient debugging. These metrics helped identify and address unintended behaviors during training.
Results
- Successfully trained robotic arm to autonomously grasp and lift a cube
- Implemented effective reward shaping to guide step-by-step task completion
- Resolved unintended behaviors through iterative reward function refinement
- Developed enhanced observation metrics for improved debugging and monitoring
- Achieved stable training performance through hyperparameter optimization
Technical Details
- Used PyTorch for deep reinforcement learning implementations
- Implemented PPO algorithm for continuous control tasks
- Leveraged MuJoCo physics engine for realistic simulation
- Developed modular codebase structure with separate config, controller, environment, and RL components
- Created custom reward functions with intermediate rewards and penalties
- Implemented data logging and observation metrics for training analysis
- Optimized hyperparameters including learning rate, discount factor, and batch size