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

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 …

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 …

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 …

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 …

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) …

Abstract Learning from Demonstration (LfD) is an efficient technique for robots to acquire
new skills through expert observation, significantly mitigating the need for laborious manual …

Language Models and Vision Language Models have recently demonstrated
unprecedented capabilities in terms of understanding human intentions, reasoning, scene …

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