policy
HumanoidClimb-RL
s1ddh-rth · PyTorch
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Overview
Name
HumanoidClimb-RL
Author
s1ddh-rth
Framework
PyTorch
License
unknown
Skill type
navigation
Evidence level
untested
Task description
This project explores the application of reinforcement learning (RL) to train humanoid robots for dynamic rock climbing movements, focusing on achieving the challenging "dyno" maneuver. Using the Proximal Policy Optimization (PPO) algorithm, the simulation integrates physics-based environments to mo
Spaces
Action space
other · 0-dim · 0Hz
Observation space
- type: other
Links
HuggingFace repo
null
Paper (arXiv)
null
Compatible robots
3+17 mentioned but not in catalog yetCompatible environments
0No environments list HumanoidClimb-RL yet.
Datasets that reference this policy
0No datasets reference HumanoidClimb-RL yet.