Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate
adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the …

One of the great promises of robot learning systems is that they will be able to learn from
their mistakes and continuously adapt to ever-changing environments, but most robot …

Abstract This paper introduces Hierarchical Diffusion Policy (HDP) a hierarchical agent for
multi-task robotic manipulation. HDP factorises a manipulation policy into a hierarchical …

Parameterized movement primitives have been extensively used for imitation learning of
robotic tasks. However, the high-dimensionality of the parameter space hinders the …

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine
learning models can solve specific downstream tasks either zero-shot or with small task …

The process of learning a manipulation task depends strongly on the action space used for
exploration: posed in the incorrect action space, solving a task with reinforcement learning …

Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to
new downstream tasks due to the innate task-specific training paradigm. To alleviate it …

General-purpose robotic systems must master a large repertoire of diverse skills to be useful
in a range of daily tasks. While reinforcement learning provides a powerful framework for …

Interactions with articulated objects are a challenging but important task for mobile robots.
To tackle this challenge, we propose a novel closed-loop control pipeline, which integrates …

The field of visual representation learning has seen explosive growth in the past years, but
its benefits in robotics have been surprisingly limited so far. Prior work uses generic visual …