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The main features of arnold-benchmark/arnold are: Simulation Frameworks.
Projects with overlapping indexed features include: askforalfred/alfred — A Benchmark for Interpreting Grounded Instructions for Everyday Tasks Mohit Shridhar, Jesse Thomason, Daniel Gordon,… facebookresearch/habitat-sim — Habitat-sim is a high-performance 3D simulation platform designed for training and benchmarking embodied AI agents… genesis-embodied-ai/genesis — Genesis is an embodied AI simulation platform and parallelized robotics simulator designed for training… haosulab/maniskill — ManiSkill is a GPU-accelerated robot simulation framework designed for training robotic manipulation skills,… mila-iqia/babyai. minedojo/minedojo — Building Open-Ended Embodied Agents with Internet-Scale Knowledge.
Habitat-sim is a high-performance 3D simulation platform designed for training and benchmarking embodied AI agents within photorealistic indoor and outdoor environments. It serves as a simulator for AI and robotics, providing a system for generating synthetic data and simulating physical interactions. The project is distinguished by a native C++ core that enables high-throughput simulation and a rendering pipeline using physically based rendering and baked global illumination. It features a navigation system based on pre-computed navigation meshes to ensure collision-free traversal and a rigi
Genesis is an embodied AI simulation platform and parallelized robotics simulator designed for training general-purpose robotic agents. It integrates a physics engine for robotics that calculates collisions and movements for rigid bodies, soft tissues, and fluids, alongside a photorealistic 3D rendering engine. The platform features a domain randomization framework to vary environment parameters across parallel simulations, aiding in sim-to-real transfer. It supports the integration of real-world captured light fields and Gaussian splatting to provide photorealistic backgrounds within simulat
ManiSkill is a GPU-accelerated robot simulation framework designed for training robotic manipulation skills, benchmarking learning algorithms, and generating synthetic datasets. It serves as a reinforcement learning environment where robot control policies can be developed and evaluated using parallelized physics and rendering on the GPU. The platform is distinguished by its ability to perform sim-to-real transfer, allowing policies trained in virtual environments to be deployed onto physical robotic hardware. It features ray-traced parallel rendering for producing high-frame-rate RGBD and se
A Benchmark for Interpreting Grounded Instructions for Everyday Tasks Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, Dieter Fox CVPR 2020