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Open textbook for upper-level undergraduates that explains computational principles behind autonomous robots — mechanisms, sensors, actuators, perception, and planning — with exercises and simulation assets. Distributed as LaTeX source under a CC-BY-NC-ND license and accompanied by course materials and Webots examples.
Readable, minimal-dependency Python implementations of core robotics algorithms — localization (EKF, particle filter), SLAM (ICP, FastSLAM), path planning (A*, RRT*, PRM), and path tracking (LQR, MPC) — written to be studied, not just run.
Compiles plain Python functions into GPU or CPU kernels at runtime via a JIT decorator, with differentiable output that plugs into PyTorch, JAX, and Paddle. Ships physics, robotics, geometry, and FEM primitives — particles, meshes, ray-casting, FFT.
Grounds language-based robot planning in learned skill value functions, selecting actions that are both useful and feasible. PaLM-SayCan reports 84% planning and 74% execution success across 101 real-world instructions.
Turns language commands into executable robot policies that combine perception, control APIs, loops, and geometry libraries. Hierarchical code generation recursively fills in missing functions to support new behaviors without policy retraining.
GPU-accelerated robot-learning framework on NVIDIA Isaac Sim, running thousands of parallel environments on one GPU for reinforcement and imitation learning. Ships 30+ ready-to-train tasks and 16+ robot models wired to RSL RL, SKRL, and RL Games.
Provides a scalable physics-and-rendering simulation interface for robotics and embodied-AI research — unified multi-physics solvers, the Nyx renderer, and the Quadrants compiler. Runs from laptop to datacenter GPUs; suited for sensor-rich data generation and RL/robotics prototyping.
GPU-native physics engine unifying rigid-body, fluid, cloth, and deformable solvers in one Python framework for robotics and embodied-AI research. Built by a 20+ lab collaboration, now backed by Genesis AI, with generative tools to author 4D scenes.
Estimates and tracks 6D poses of novel objects without per-object fine-tuning — supports both model-based (CAD) and model-free (few reference images) setups. Trained on large-scale synthetic data with a transformer-based architecture and contrastive learning; CVPR 2024 highlight with demos and pretrained weights.
A PyTorch-native, hardware-agnostic stack for robot learning: data collection, training, and deployment across 11+ robots, from SO100 to Unitree G1. Includes imitation, RL, and vision-language-action policies (ACT, Diffusion, Pi0, SmolVLA).
GPU‑accelerated framework for training physically simulated humanoid characters and robots using reinforcement learning and motion imitation. Provides a modular multi‑backend simulator stack, large‑scale multi‑GPU training recipes, built‑in motion retargeting and an ONNX deployment pathway to real robots.