Domain randomization and generative models for robotic grasping

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

Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?

arXiv:2606. 26428v1 Announce Type: cross Abstract: Multi-fingered robots promise the speed and dexterity of human hands, yet challenging problems such as precise assembly have remained out of reach.

Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty

arXiv:2604. 25897v2 Announce Type: replace-cross Abstract: Contact variability, sensing uncertainty, and external disturbances make grasp execution stochastic.

Solving Rubik’s Cube with a robot hand

We’ve trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR).

Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)

arXiv:2509. 06191v2 Announce Type: replace-cross Abstract: Recent 3D generative models, which are capable of generating full object shapes from just a few images, now open up new opportunities in robotics.

Grasp-Then-Plan with Failure Attribution: A Closed Two-Stage Framework for Precise and Generalizable Robotic Manipulation

arXiv:2606. 03385v1 Announce Type: cross Abstract: In robotic manipulation, the tight coupling between grasping and motion planning often obscures the true source of failure, leading to inefficient trial-and-error.