Skip to main content

IsaacGym

Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
 Paper arXiv Project Page

IsaacGym

NVIDIA’s physics simulation environment for reinforcement learning research.

Official Materials​

IsaacGymEnvs​

This repository contains example RL environments for the NVIDIA Isaac Gym high performance environments described in NVIDIA's NeurIPS 2021 Datasets and Benchmarks paper.

Bi-DexHands​

Bi-DexHands provides a collection of bimanual dexterous manipulation tasks and reinforcement learning algorithms. Reaching human-level sophistication of hand dexterity and bimanual coordination remains an open challenge for modern robotics researchers.

DexPBT​

DexPBT implements challenging tasks for one- or two-armed robots equipped with multi-fingered hand end-effectors, including regrasping, grasp-and-throw, and object reorientation. It also introduces a decentralized Population-Based Training (PBT) algorithm that massively amplifies the exploration capabilities of deep reinforcement learning.

TimeChamber​

TimeChamber is a large scale self-play framework running on parallel simulation. Running self-play algorithms always need lots of hardware resources, especially on 3D physically simulated environments. TimeChamber provides a self-play framework that can achieve fast training and evaluation with ONLY ONE GPU.