… We broadly address what-to-learn and how-to-learn problem in the context of robot learning …
The operator controls/teleoperates the remote arm with a simulated delay using the other …

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

… towards scaling up robot learning across diverse tasks and environments. We propose
Generation to Simulation (Gen2Sim), a method for scaling up robot skill learning in simulation by …

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

… We believe generative models of images and language will play an important 350 … robot
training data in simulation, and in crossing the sim2real 351 gap, necessary for delivering robot …

… robot learning frameworks that present promising results. These include: reinforcement learning
[31] - where a robot learns … on a series of challenging simulated robotics tasks described …

… 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 …

… —with a different robot and simulator—previous work that has shown that HyperNEAT is an
effective encoding for automatically evolving coordinated, regular gaits in simulation [4, 7, 25]…

… Generative pre-training (GPT) is a transformer-based model … learning in the simulation
since they include enormous robot … lower the threshold, attracting beginners in robot learning. …

… 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 …