AI For Robotics Physical Interaction

Manipulation refers to the physical control an agent has over its environment through selective contact. Partially observable and unpredictable environments pose interesting challenges that are currently only overcome in controlled settings like factories. To accomplish a wide range of tasks in our everyday spaces, a robot needs to be able to safely manipulate objects not only in factories but also in uncontrolled environments such as our homes, in shops, public areas and in offices. To enable this, we aim at developing novel methods to handle a broader spectrum of manipulation tasks and contexts.




Risk Sensitive Robot Navigation
We explore how effectively a single policy learned by reinforcement learning can modulate robot behaviour, from risk-averse (cautious) to risk-neutral (maximizing the average reward). Blog by Chris Dance et al.
A first-of-its-kind architecture that, based on a single image, predicts how a robot can pick up objects from within any scene could revolutionize applications in AR/VR and robotics. Blog article by Gregory Rogez
DCRL A new family of approaches to few-shot imitation
A new family of approaches to few-shot imitation: demonstration-conditioned reinforcement learning. Blog by Theo Cachet, Julien Perez and Chris Dance.
Fast Adaptation Blog Image 1
Our navigation system enables robots to adapt to specific real-world environments and use cases with only small amounts of human preference data. Blog article by Christopher Dance and Jinyoung Choi
Julien Perez podcast
In this podcast, Julien Perez talks about the 3rd Robot Learning Workshop which he co-organised at NeurIPS 2020.
HANDS19 Challenge, a public competition designed for the evaluation of the task of 3D hand pose estimation in both depth and colour modalities in the presence and absence of objects.

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