# Robot Motion Control

This model is a vision-language-action model fine-tuned on the bridge dataset based on the SpatialVLA model, specifically designed for the Simpler-env benchmark.

OpenVLA v0.1 7B is an open-source vision-language-action model trained on the Open X-Embodiment dataset, supporting various robot controls.

This is a reinforcement learning model based on the SAC algorithm, designed to control robot hopping movements in the Hopper-v3 environment.

This is a TD3 agent model trained using the stable-baselines3 library, specifically designed for reinforcement learning tasks in the Hopper-v3 environment.

This is a TD3 reinforcement learning agent trained using the stable-baselines3 library, specifically designed for the HalfCheetah-v3 environment, achieving an average reward of 9709.01.

This is a trained decision transformer model, with training data derived from expert trajectories sampled in the Gym Walker2d environment.