dm_control is a physics-based simulation framework and robot control simulation toolkit designed for creating and interacting with continuous control tasks. It serves as a suite of reinforcement learning environments and a benchmarking tool for evaluating autonomous agents within virtual physics spaces.
The main features of google-deepmind/dm_control are: Physical Interaction Simulations, State-Action-Reward Interfaces, Reinforcement Learning, Physics Simulation, Robotics Simulators, State-Based Physics, Physics Simulation Environments, Rigid Body Physics Engines.
Open-source alternatives to google-deepmind/dm_control include: facebookresearch/habitat-sim — Habitat-sim is a high-performance 3D simulation platform designed for training and benchmarking embodied AI agents… cyberbotics/webots — Webots is a physics-based robot simulator and development environment used for modeling, programming, and testing the… openmind/om1 — OM1 is a multimodal AI agent runtime and orchestration framework designed to connect large language models to physical… farama-foundation/gymnasium — Gymnasium is a suite of standardized APIs and simulation toolkits used to evaluate agent behavior and benchmark… dlr-rm/stable-baselines3 — Stable-baselines3 is a reinforcement learning library built on the PyTorch deep learning framework. It provides a… projectchrono/chrono — Chrono is a multi-physics simulation suite that functions as a multibody dynamics simulator, a finite element analysis…
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
Webots is a physics-based robot simulator and development environment used for modeling, programming, and testing the behavior of robots in a simulated 3D physical world. It serves as a virtual prototyping tool to verify mechanical and electronic systems through the creation of virtual robot models and control logic. The platform enables a full robotics simulation workflow, including the development of robot controllers and the programming of autonomous agent behaviors. It focuses on physical system modeling to represent the mechanical properties of hardware and simulate real-world interactio
OM1 is a multimodal AI agent runtime and orchestration framework designed to connect large language models to physical robot hardware and sensors. It provides an execution environment that processes audio, video, and sensor data to drive autonomous decisions and actions in real-world settings. The system integrates a robotics SLAM and navigation stack with a hardware abstraction layer, allowing high-level AI commands to be translated into low-level motor and actuator instructions. It distinguishes itself by incorporating blockchain-based governance to enforce immutable operational rules and p
Gymnasium is a suite of standardized APIs and simulation toolkits used to evaluate agent behavior and benchmark reinforcement learning algorithms. It provides a standardized interface for creating and interacting with simulated environments, enabling the training of reinforcement learning agents through a consistent set of interaction protocols. The project emphasizes experimental reproducibility through a versioned API and a system for tracking changes to environment logic using version suffixes. This ensures that learning results remain consistent and can be replicated across different soft