s1ddh-rth/HumanoidClimb-RL
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 model realistic climbing scenarios.
This project helps robotics researchers and engineers train humanoid robots to perform complex rock climbing movements, specifically dynamic transfers known as 'dynos.' You input simulation parameters for a climbing environment, and it outputs trained models that dictate how a virtual humanoid robot moves its limbs to climb. This is for researchers developing advanced robotic control systems for bipedal or multi-limbed robots.
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Use this if you are a robotics researcher aiming to develop and evaluate reinforcement learning algorithms for dynamic, multi-limb robotic locomotion in complex environments.
Not ideal if you need a plug-and-play solution for real-world robot deployment or if your focus is on static stability rather than dynamic movement.
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Python
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Last pushed
Oct 25, 2024
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