Humanoid Robot Learning

Overview

66% training time reduction for humanoid robot control. Enhanced TDMPC2 algorithm with decision transformers while maintaining performance benchmarks.

Problem

Training humanoid robots for complex motor control tasks requires significant computational time. Existing RL algorithms face efficiency bottlenecks.

Solution

Implemented transformer-based reinforcement learning in PyTorch, integrating decision transformers with TDMPC2. Validated efficiency gains across DreamerV3 and other algorithms in MuJoCo simulation for humanoid sit task.

Impact

• 66% faster training: Accelerated learning for motor control
• Maintained performance: Efficiency without sacrificing benchmarks
• Algorithm innovation: Transformer integration with model-based RL
• Open source: Published codebase for research community