Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …

Imitation learning from human demonstrations is a promising paradigm for teaching robots
manipulation skills in the real world. However, learning complex long-horizon tasks often …

Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient
robot learning from visual observations. Yet the current approaches typically train a single …

To build general robotic agents that can operate in many environments, it is often imperative
for the robot to collect experience in the real world. However, this is often not feasible due to …

This article presents a literature review of the past five years of studies using Deep
Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic …

We aim to endow a robot with the ability to learn manipulation concepts that link natural
language instructions to motor skills. Our goal is to learn a single multi-task policy that takes …

Abstract Setting up a well-designed reward function has been challenging for many
reinforcement learning applications. Preference-based reinforcement learning (PbRL) …

Theory of mind (ToM) is the psychological construct by which we model another's internal
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …

Imitation learning (IL) aims to extract knowledge from human experts' demonstrations or
artificially created agents to replicate their behaviors. It promotes interdisciplinary …

For robots to be useful outside labs and specialized factories we need a way to teach them
new useful behaviors quickly. Current approaches lack either the generality to onboard new …