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Cyclo is an open, modular framework for building and operating Physical AI systems across AI integration, data workflows, robot control, simulation, and operations on real hardware.
Tien Kung-Lab: Direct IsaacLab Workflow for Legged Robots
Official implementations for "Action2Motion: Conditioned Generation of 3D Human Motions (ACM MultiMedia 2020)"
Official implementation of ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation (SIGGRAPH 2026).
The official PyTorch implementation of the paper "Human Motion Diffusion Model"
Yet Another Analytical Inverse Kinematics Generator
Converts a 3D Point Cloud into a 2D laser scan.
[ICRA 2026] Agility Meets Stability: Versatile Humanoid Control with Heterogeneous Data
GR00T-VisualSim2Real: Open-source sim-to-real framework for humanoid visual loco-manipulation. Train in simulation, deploy zero-shot on real robots with RGB + proprioception for tasks like pick-and…
Code to train a compliant whole-body controller for the Unitree B1 + Z1
egui: an easy-to-use immediate mode GUI in Rust that runs on both web and native
RL environments and PPO training code to develop trajectory tracking controllers for various robot systems, written in Jax.
This repository contains the code of the paper SO(2)-Equivariant Reinforcement Learning (ICLR 2022) and On-Robot Learning With Equivariant Models (CoRL 2022).
Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/
Allex robot model assets (URDF, MJCF, USD, meshes)
Official implementation of Kimodo, a kinematic motion diffusion model for high-quality human(oid) motion generation.
Code for DreamControl: Human-inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusion
GentleHumanoid: Whole body Motion Tracking with Compliance - Inference and Deploy
GentleHumanoid: Whole Body Motion Tracking with Compliance - Training
Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation
TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos
Code for the project "MegaSaM: Accurate, Fast and Robust Structure and Motion from Casual Dynamic Videos"