Deep learning of deformable object manipulation on manufacturing robots

Background

Current robots in manufacturing settings are not able to adapt to variations introduced by collaborating humans or other factors in the environment. This is especially true when the robot has to manipulate objects that are deformable and flexible.

However, the real time cognitive ability of humans and animals provide us a lot of inspiration for creating resilient and robust artificial intelligent systems that optimise in real time to problems and variations in their environment especially when dealing with deformable objects. A potential research question is: “How can the skills learnt in one task be transferred across to another task involving deformable objects?” The successful candidate will work with a team of researchers and industrial partners on the project DigiCORTEX (https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/W014688/1)

Methodology

One of the paradigms that has been created by the machine learning community is the Reinforcement Learning paradigm. In reinforcement learning, the focus is on applying rewards from the environment to learn as well as finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) to find solutions to problems. The paradigm relies on a prior well-defined environment and results in a control policy that is best suited for the environment that it was designed for. It starts suffering when there are variations such as the ones introduced by deformable objects in the environment.

Aim and objectives

Informed by cognitive psychology literature and supported by neuro-evolution ^[1], meta-learning, neural architecture search methodologies ^[2][3] as well as recent work in reinforcement learning for deformable object manipulations ^[4][5], this PhD topic aims to develop new real time cognitive approaches and computation to create robots that can flexibly learn new ways of manipulating deformable objects and transfer learnt tasks to new settings.

Objectives

1. Investigate various frameworks that support how humans interact with deformable objects.  
2. Develop a framework that embeds meta-learning and neural architecture search methodologies
3. Develop a robotics simulation environment for various use cases that deal with deformable objects. The successful student would work with our industrial partners to create various use cases that suit their industrial needs. You will have an opportunity to collaborate with our industrial partners on various use cases in sectors such as Manufacturing, Space, Agriculture etc.
4. Integrate objectives 2 and 3 into the development of new computational intelligent algorithms.
5. Validate algorithms on 2 collaborative robot platforms. You will have access to various robot platforms including the Franka arm, Sawyer, UR5 and UR15.   

This work will be carried out in the Department of Computer Science (top 10 in UK for research quality), YorRobots (https://www.york.ac.uk/yorrobots/) and at the University of York’s £15m institute for safe autonomy (https://www.york.ac.uk/safe-autonomy/). The Institute for Safe Autonomy is UK’s first research centre dedicated to the design, development, safety and communications for robotics and connected autonomous systems. The Institute provides a world-leading ecosystem for research and innovation, education, public engagement and commercial realisation. You will also be expected to collaborate with our industrial partners on various use cases from time to time.

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