Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic grasping and manipulation. Current trends and …

To solve tasks in complex environments, robots need to learn from experience. Deep
reinforcement learning is a common approach to robot learning but requires a large amount …

The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …

In this paper, we explore the potential of using a large language model (LLM) to understand
the driving environment in a human-like manner and analyze its ability to reason, interpret …

Animals are capable of precise and agile locomotion using vision. Replicating this ability
has been a long-standing goal in robotics. The traditional approach has been to decompose …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Simulation-based reinforcement learning approaches are leading the next innovations in
legged robot control. However, the resulting control policies are still not applicable on soft …

Successful real-world deployment of legged robots would require them to adapt in real-time
to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper …

Legged locomotion can extend the operational domain of robots to some of the most
challenging environments on Earth. However, conventional controllers for legged …