Large Language Models (LLMs) have excelled as high-level semantic planners for
sequential decision-making tasks. However, harnessing them to learn complex low-level …

Isaac Gym offers a high performance learning platform to train policies for wide variety of
robotics tasks directly on GPU. Both physics simulation and the neural network policy …

Recent work has demonstrated the ability of deep reinforcement learning (RL) algorithms to
learn complex robotic behaviours in simulation, including in the domain of multi-fingered …

We present Orbit, a unified and modular framework for robot learning powered by Nvidia
Isaac Sim. It offers a modular design to easily and efficiently create robotic environments …

There has been significant progress in developing reinforcement learning (RL) training
systems. Past works such as IMPALA, Apex, Seed RL, Sample Factory, and others, aim to …

Deep reinforcement learning can generate complex control policies, but requires large
amounts of training data to work effectively. Recent work has attempted to address this issue …

Robotic assembly is one of the oldest and most challenging applications of robotics. In other
areas of robotics, such as perception and grasping, simulation has rapidly accelerated …

In-hand manipulation of objects is an important capability to enable robots to carry-out tasks
which demand high levels of dexterity. This work presents a robot systems approach to …

Robotic assembly is a longstanding challenge, requiring contact-rich interaction and high
precision and accuracy. Many applications also require adaptivity to diverse parts, poses …

In this work, we propose algorithms and methods that enable learning dexterous object
manipulation using simulated one-or two-armed robots equipped with multi-fingered hand …