haosulab/ManiSkill

Features

• GPU-Accelerated Robot Simulators - Provides a GPU-accelerated physics simulation environment specifically designed for training and testing robotic manipulation policies.
• Articulated Body Simulators - Executes GPU-parallelized rigid-body simulations of articulated robot chains and grippers for manipulation tasks.
• Reinforcement Learning Environments - Serves as a simulation suite for developing and evaluating robot control policies through reinforcement learning.
• Parallel Simulation Environments - Simulates multiple unique environments in parallel to accelerate experience gathering for reinforcement learning.
• Imitation and Reinforcement Learning Toolkits - Provides a toolkit for training robotic behaviors using both imitation learning and reinforcement learning baselines.
• Sim-to-Real Robot Policy Trainings - Translates control policies trained in virtual environments into executable commands for physical robotic hardware.
• Policy Deployments - Transitions trained robotic control policies to physical hardware for real-world execution.
• Domain Randomizations - Varies physical properties and visual textures across simulations to improve the robustness of learned policies.
• Sim-to-Real Transfer - Bridges the gap between simulated training environments and physical hardware deployment for robotic policies.
• Sim-to-Real Transfer - Offers a framework for training robotic behaviors in simulation and deploying those policies to physical hardware.
• High-Quality Scene Rendering - Generates high-quality visual output of simulations using ray-traced shaders in both graphical and headless modes.
• Parallel Ray Tracing - Generates high-frame-rate RGBD and segmentation data using GPU-accelerated parallel ray tracing across multiple scenes.
• Synthetic Dataset Generators - Automatically generates synthetic RGBD and segmentation datasets across parallel simulations for training vision models.
• Robot Embodiment Registrations - Supports switching between different robot hardware by loading pre-configured models with standardized collision and controller data.
• Scalable Robot Policy Evaluations - Enables scalable and repeatable evaluation of trained robot policies within accelerated simulation environments.
• Robot Policy Validations in Simulation - Validates trained robot policies in physically accurate virtual environments prior to physical hardware deployment.
• Environment Definitions - Provides an object-oriented interface for creating simulation environments and managing GPU memory for robotic tasks.
• Synthetic Robotic Dataset Generators - Produces high-frame-rate RGBD and segmentation data across diverse parallelized virtual scenes for dataset generation.
• Robot Model Libraries - Loads pre-configured robot models including collision data and controllers for simulation environments.
• Embodiment Management - Enables switching between different robot models to evaluate skill generalization across hardware configurations.
• Robotics Benchmarks - Provides a collection of standardized manipulation environments to benchmark the performance of robot learning algorithms.
• Robotic Sensor Simulation - Simulates sensor limitations by toggling between ground-truth state data and restricted visual observations.
• Parallel Visual Data Capture - Captures high-frame-rate RGBD and segmentation data using GPU parallelization for dataset generation.
• Sensor Observation Toggles - Toggles between raw ground-truth state data and simulated sensor observations to control model information.
• Simulation and Benchmarking - GPU-parallelized simulation for generalizable embodied AI.
• Simulation Frameworks - GPU-parallelized simulation for generalizable embodied AI.
• Simulation Engines - Robot simulation and manipulation learning package.

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