OpenAI Blog
May 16, 2017
Robots that learn
We’ve created a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing it done once.
Generalizing from simulation
Our latest robotics techniques allow robot controllers, trained entirely in simulation and deployed on physical robots, to react to unplanned changes in the environment as they solve simple tasks. That is, we’ve used these techniques to build closed-loop systems rather than open-loop ones as before.
Related stories
OpenAI Blog
Feb 26, 2018
Ingredients for robotics research
We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for our research over the past year. We’ve used these environments to train models which work on physical robots.
arXiv AI
Jul 22
The Open Ant: A Robot Platform for Reinforcement Learning Research
arXiv:2607. 18488v1 Announce Type: cross Abstract: Reinforcement learning (RL) research has demonstrated success in both physical and simulated domains; however, the predominant methodology remains rooted in simulations.
OpenAI Blog
May 15, 2017
Roboschool
We are releasing Roboschool: open-source software for robot simulation, integrated with OpenAI Gym.
arXiv AI
Jun 2
Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX
arXiv:2606. 01478v1 Announce Type: cross Abstract: High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms.
arXiv AI
6d ago
A Conceptual Framework for Refining Influence Knowledge from Simulation Evidence in Cyber-Physical Systems
arXiv:2608. 11221v1 Announce Type: new Abstract: Cyber-physical systems (CPS) are typically developed by multiple stakeholders who produce artefacts tailored to their specific domains of expertise.
OpenAI Blog
Oct 18, 2017
Sim-to-real transfer of robotic control with dynamics randomization
arXiv AI
Jul 2
ASPIRE: Agentic /Skills Discovery for Robotics
arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.
arXiv AI
Jun 19
ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
arXiv:2606. 19980v1 Announce Type: new Abstract: Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence.
arXiv AI
Jul 24
What Matters for Simulation to Online Reinforcement Learning on Real Robots
arXiv:2602. 20220v2 Announce Type: replace-cross Abstract: We investigate what specific design choices enable successful online reinforcement learning (RL) on physical robots.
arXiv AI
Jun 10
A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI
arXiv:2505. 01458v2 Announce Type: replace-cross Abstract: Navigation and manipulation are core capabilities in Embodied AI, but training agents to perform them directly in the real world is costly, time-consuming, and unsafe.
arXiv AI
Jun 3
Too Much of a Good Thing: When sim2real Efforts Impede Policy Learning (And What to Do About It)
arXiv:2606. 02636v1 Announce Type: cross Abstract: While sim2real efforts are necessary for effective policy transfer to hardware, there is such a thing as too much of a good thing.