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David Barker

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3 papers

2 author rows

TMLR Journal 2024 Journal Article

RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Konstantinos Bousmalis
Giulia Vezzani
Dushyant Rao
Coline Manon Devin
Alex X. Lee
Maria Bauza Villalonga
Todor Davchev
Yuxiang Zhou

The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learning. Inspired by recent advances in foundation models for vision and language, we propose a multi-embodiment, multi-task generalist agent for robotic manipulation. This agent, named RoboCat, is a visual goal-conditioned decision transformer capable of consuming action-labelled visual experience. This data spans a large repertoire of motor control skills from simulated and real robotic arms with varying sets of observations and actions. With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100–1000 examples for the target task. We also show how a trained model itself can be used to generate data for subsequent training iterations, thus providing a basic building block for an autonomous improvement loop. We investigate the agent’s capabilities, with large-scale evaluations both in simulation and on three different real robot embodiments. We find that as we grow and diversify its training data, RoboCat not only shows signs of cross-task transfer, but also becomes more efficient at adapting to new tasks.

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ICRA Conference 2019 Conference Paper

A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning

Mel Vecerík
Oleg Sushkov
David Barker
Thomas Rothörl
Todd Hester
Jonathan Scholz

Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be highly effective when task geometry is known, but are complex and cumbersome to implement, and must be tailored to each individual problem by a qualified engineer. Within the learning community there is a long history of insertion research, but existing approaches are either too sample-inefficient to run on real robots, or assume access to high-level object features, e. g. socket pose. In this paper we show that relatively minor modifications to an off-the-shelf Deep-RL algorithm (DDPG), combined with a small number of human demonstrations, allows the robot to quickly learn to solve these tasks efficiently and robustly. Our approach requires no modeling or simulation, no parameterized search or alignment behaviors, no vision system aside from raw images, and no reward shaping. We evaluate our approach on a narrow-clearance peg-insertion task and a deformable clip-insertion task, both of which include variability in the socket position. Our results show that these tasks can be solved reliably on the real robot in less than 10 minutes of interaction time, and that the resulting policies are robust to variance in the socket position and orientation.

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ICML Conference 2013 Conference Paper

Concurrent Reinforcement Learning from Customer Interactions

David Silver 0001
Leonard Newnham
David Barker
Suzanne Weller
Jason McFall

In this paper, we explore applications in which a company interacts concurrently with many customers. The company has an objective function, such as maximising revenue, customer satisfaction, or customer loyalty, which depends primarily on the sequence of interactions between company and customer. A key aspect of this setting is that interactions with different customers occur in parallel. As a result, it is imperative to learn online from partial interaction sequences, so that information acquired from one customer is efficiently assimilated and applied in subsequent interactions with other customers. We present the first framework for concurrent reinforcement learning, using a variant of temporal-difference learning to learn efficiently from partial interaction sequences. We evaluate our algorithms in two large-scale test-beds for online and email interaction respectively, generated from a database of 300, 000 customer records.

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