… , a generative robotic agent that automatically learns diverse robotic skills at scale via generative
simulation… RoboGen leverages the latest advancements in foundation and generative …

… These simulators are parameterized generative models, which describe how multiple
bodies or particles evolve over time by interacting with each other. The associated physics …

… the simulation-to-reality-gap, in this paper a methodology for the real-time generation of
realistic images in robotic simulation … Hadsell, “Sim-to-real robot learning from pixels with …

… automatically translate simulated observations into realistic ones via a generative adversarial
… We evaluate simulation-to-real methods for robotic grasping in a scenario where off-policy …

… for robot trajectory generation. In our experiments, we show that our approach can achieve
continual learning ability in both simulation … Metta, “Incremental robot learning of new objects …

… potentials that, unlike the generative models that we make … range of simulations as well as
via real robot experiments on … our method both in simulation and on a real robot. Our aim is …

… Deep learning networks along with physics-based simulators have also been used to study
… of the robotic manipulators used in this paper. A brief introduction of generative adversarial …

… We propose a new method for simulation identification, in which a Generative Adversarial
Network (GAN) [3] … Stone, “Humanoid robots learning to walk faster: From the real world to …

… We validate the approach in simulation using the 7-… learning techniques in robotics, eg,
[5], we propose to adapt GAN to the context of robotics, ie, to learn the distribution of valid robot …

… simulations in Pybullet [21]. We evaluate our method by querying them with randomly sampled
terrains and simulating … of the generated designs in simulation. We conducted all training …