Stars
Improved Rule-Based Lloyd algorithm. Distributed algorithm for multi-uav communication-free coordination in complex environment
A lightweight suite of motion imitation methods for training controllers.
Isaac Lab API, powered by MuJoCo-Warp, for RL and robotics research
Interactive Character Control with Auto-Regressive Motion Diffusion Models
Training and testing scripts for the prediction model used in the "Interaction-Aware Sampling-Based MPC with Learned Local Goal Predictions" paper.
Unofficial implementation of the Dreamer 4 world model in PyTorch.
Algorithms for Decision Making textbook
🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX
Octo is a transformer-based robot policy trained on a diverse mix of 800k robot trajectories.
Simple and unified interface to zero-shot computer vision models curated for robotics use cases.
Wrappers for ROS visualization, benchmarking and data saving in C++
🤖 The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation
Elucidating the Design Space of Diffusion-Based Generative Models (EDM)
Barkour Robot: Agile Quadruped Robots by Google DeepMind
Dream to Control: Learning Behaviors by Latent Imagination, implemented in PyTorch.
BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.
Pytorch re-implementation of the paper "Multi-agent path integral control for interaction-aware motion planning in urban canals" by Lucas Streichenberg, Elia Trevisan, Jen Jen Chung, Roland Siegwar…
Random Network Distillation pytorch
[ICRA 2024]: Train your parkour robot in less than 20 hours.
Multi-Agent Reinforcement Learning (MARL) method to learn scalable control polices for multi-agent target tracking.
An implementation of 1D, 2D, and 3D positional encoding in Pytorch and TensorFlow
[CVPR2023] Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration
Code for the paper "When to Trust Your Model: Model-Based Policy Optimization"
Inference of resilient properties with probabilistic graphical models