LatentMimic Framework Enables Quadruped Robots to Adapt Locomotion to Complex Terrains
LatentMimic is an innovative framework designed for locomotion learning in quadruped robots, tackling the difficulty of developing controllers that naturally adjust to intricate terrains while maintaining a consistent movement style. Current approaches often face a dilemma between following motion capture references and adapting to different terrains. By reducing the marginal latent divergence between the learned mocap prior and the policy's state-action distribution, LatentMimic facilitates independent adaptations of end-effectors. Additionally, it features a terrain adaptation module with dynamic elements. This research, published on arXiv (arXiv:2604.16440v1), seeks to improve robotic locomotion, enabling robots to modify their movements according to terrain changes while preserving stylistic integrity, potentially enhancing their versatility in unpredictable environments.
Key facts
- LatentMimic is a novel locomotion learning framework for quadruped robots
- It decouples stylistic fidelity from geometric constraints in robot locomotion
- The framework minimizes marginal latent divergence between policy distribution and mocap prior
- It provides conditional relaxation of rigid pose-tracking objectives
- LatentMimic preserves gait topology while allowing end-effector adaptations for irregular terrains
- The research addresses optimization trade-offs in existing imitation-based methods
- The work was announced on arXiv with identifier arXiv:2604.16440v1
- The announcement type is cross
Entities
Institutions
- arXiv