Sim-to-real transfer of robotic control with dynamics randomization

How Should a Simulation-to-Reality Transfer Budget Be Spent?

arXiv:2606. 22062v2 Announce Type: replace-cross Abstract: Simulation-to-reality transfer, often called sim-to-real transfer, is a central challenge in robot learning.

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.

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.

TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation

arXiv:2606. 06218v1 Announce Type: cross Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot.

Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks

arXiv:2604. 09487v2 Announce Type: replace-cross Abstract: Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition.

Domain randomization and generative models for robotic grasping