Generate, Track, Improve: Perceptive Multi-Skill Humanoid Locomotion with RL-Fine-Tuned Motion Generators
cs.RO
Submitted: 2026-09-25
Updated: 2026-09-25
Terminology
Sources
- Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition
- Chasing Autonomy: Dynamic Retargeting and Control Guided RL for Performant and Controllable Humanoid Running
- Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control
- Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
- PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers
- Humanoid Parkour Learning
- RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains
- Learning Humanoid Locomotion with Perceptive Internal Model
- BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds
- Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds
- MARCH: Model-Assisted Reinforcement Learning for the Perceptive Control of Humanoids over Sparse Footholds
- SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
- UniTracker: Learning Universal Whole-Body Motion Tracker for Humanoid Robots
- Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning
- Diffuse-CLoC: Guided Diffusion for Physics-based Character Look-ahead Control
- Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
- Proximal Policy Optimization Algorithms
- DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
- BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion
- ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control
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