Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

Research and application of reinforcement learning in robotics for contact-rich manipulation
tasks have exploded in recent years. Its ability to cope with unstructured environments and …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …

robosuite is a simulation framework for robot learning powered by the MuJoCo physics
engine. It offers a modular design for creating robotic tasks as well as a suite of benchmark …

We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation,
spanning a range of everyday household chores such as cleaning, maintenance, and food …

We tackle the problem of learning complex, general behaviors directly in the real world. We
propose an approach for robots to efficiently learn manipulation skills using only a handful of …

In this survey, we present the current status on robots performing manipulation tasks that
require varying contact with the environment, such that the robot must either implicitly or …

Generalizable manipulation skills, which can be composed to tackle long-horizon and
complex daily chores, are one of the cornerstones of Embodied AI. However, existing …

Contact-rich manipulation tasks in unstructured environments often require both haptic and
visual feedback. It is nontrivial to manually design a robot controller that combines these …