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 …

Simulation is an essential tool to develop and benchmark autonomous vehicle planning
software in a safe and cost-effective manner. However, realistic simulation requires accurate …

Building general-purpose robots that operate seamlessly in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

Throughout history, we have successfully integrated various machines into our homes.
Dishwashers, laundry machines, stand mixers, and robot vacuums are a few recent …

Deploying robots in open-ended unstructured environments such as homes has been a long-
standing research problem. However, robots are often studied only in closed-off lab settings …

Large Language Models (LLMs) have been shown to be capable of performing high-level
planning for long-horizon robotics tasks, yet existing methods require access to a pre …

A promising technique for exploration is to maximize the entropy of visited state distribution,
ie, state entropy, by encouraging uniform coverage of visited state space. While it has been …

Large Language Models (LLMs) have demonstrated the ability to perform semantic
reasoning, planning and write code for robotics tasks. However, most methods rely on pre …

Despite recent advances in improving the sample-efficiency of reinforcement learning (RL)
algorithms, designing an RL algorithm that can be practically deployed in real-world …

The realization of universal robots is an ultimate goal of researchers. However, a key hurdle
in achieving this goal lies in the robots' ability to manipulate objects in their unstructured …