The increasing trend of studying the innate softness of robotic structures and amalgamating
it with the benefits of the extensive developments in the field of embodied intelligence has …

A fundamental task in robotics is to plan collision‐free motions among a set of obstacles.
Recently, learning‐based motion‐planning methods have shown significant advantages in …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

In this paper we propose a novel end-to-end learnable network that performs joint
perception, prediction and motion planning for self-driving vehicles and produces …

We introduce a neural architecture for navigation in novel environments. Our proposed
architecture learns to map from first-person views and plans a sequence of actions towards …

Navigating through unstructured environments is a basic capability of intelligent creatures,
and thus is of fundamental interest in the study and development of artificial intelligence …

Many robotic applications require the agent to perform long-horizon tasks in partially
observable environments. In such applications, decision making at any step can depend on …

What is a good visual representation for navigation? We study this question in the context of
semantic visual navigation, which is the problem of a robot finding its way through a …

Abstract Model-based control is a popular paradigm for robot navigation because it can
leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is …