Weixy21/SafeDiffuser

Safe Planning with Diffusion Probabilistic Models

40
/ 100
Emerging

This project helps robotics researchers and practitioners develop and test path planning algorithms that prioritize safety. It takes data from simulated environments like mazes, robotic arm movements, or humanoid locomotion, and generates sequences of actions to guide a robot. The primary users are researchers and engineers working on safe reinforcement learning and robotic control.

No commits in the last 6 months.

Use this if you are a robotics researcher exploring diffusion models for safe robot trajectory generation in simulated environments like maze navigation or robot manipulation.

Not ideal if you are looking for an out-of-the-box solution for real-world robotic deployment or if you are not comfortable with customizing deep learning models and experimental setups.

robotics safe-reinforcement-learning motion-planning robot-control simulated-environments
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 14 / 25

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Stars

66

Forks

9

Language

Python

License

MIT

Last pushed

Apr 30, 2025

Commits (30d)

0

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