Advanced Learning Frameworks

Our training frameworks are designed to harness cutting-edge methodologies in reinforcement learning (RL) and behavior cloning (BC), ensuring high adaptability and efficiency in robot policy development.

Key Innovations:

• Hybrid Learning Framework: Combines RL with behavior cloning, enabling faster convergence on optimal policies while leveraging human-provided demonstrations for initial training phases.

• Curriculum Learning: Gradual scaling of environment complexity, allowing robots to progressively master basic tasks before tackling more challenging scenarios.

• Dynamic Reward Engineering: A tailored reward system evaluates and fine-tunes robot performance in real-time, ensuring a balance between task efficiency and energy optimization.

Performance Highlights:

• In Phase 2, rovers trained with this framework demonstrated:

  • A 37.8% improvement in mobility efficiency across complex terrains.

  • A 19.6% reduction in navigation path deviation.

  • An overall autonomous navigation success rate of 92.3%.