DAMP: Humanoid Locomotion via Denoised Belief Learning and Adversarial Motion Priors
This paper introduces DAMP, a reinforcement learning framework aimed at achieving robust and naturalistic humanoid locomotion over challenging terrains, with the assumption that no perceived information is available.
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Key points
- Humanoid robots possess the structural capability to traverse complex terrains.
- The framework leverages recurrent neural networks to capture temporal dependencies and implicitly infer privileged and other task-relevant latent information.
- By aligning the learned representations with the task objective, the method enables robust and goal-consistent policy learning.
- This end-to-end framework achieves transfer learning from simulation to real-world environments, demonstrating the proposed method's robustness and generalization capabilities.
Sources (1)
- [1]DAMP: Humanoid Locomotion via Denoised Belief Learning and Adversarial Motion PriorsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 08:42 AM
This paper introduces DAMP, a reinforcement learning framework aimed at achieving robust and naturalistic humanoid locomotion over challenging terrains, with the assumption that no perceived information is available.
Humanoid robots possess the structural capability to traverse complex terrains.
Extractive summary: sentences quoted from the sources.