ENRICHME robot demonstration in LACE elderly housing

ENRICHME robot demonstration in LACE elderly housing

We recently demonstrated the robot we are using for our ENRICHME project in one of the LACE elderly housing schemes. The video presents a summary…

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L-CAS "Open Lab" for Students and new Student-led Robotics Club

L-CAS “Open Lab” for Students and new Student-led Robotics Club

One of the research centres in the School of Computer Science is the “Lincoln Centre for Autonomous Systems“, short: L-CAS. We do a lot of…

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Fully-funded PhD scholarship for a UK/EU Student in Robotics

Fully-funded PhD scholarship for a UK/EU Student in Robotics

We have tentatively secured funding for an exciting PhD position (for UK/EU students only I’m afraid) in the area of Robotics. The successful candidate will be pursuing a PhD within the Lincoln Centre for Autonomous Systems (L-CAS) under the supervision of Dr Marc Hanheide and Prof Tom Duckett. The project is about long-term adaptation in human-robot collaboration for manufacturing applications
The studentship covers all fees, plus a stipend of £15000 per year for a duration of 3.5 years. The position is part of a recent strategic investment by the University of Lincoln, and only projects that recruit strong candidates will actually be funded. So, we need excellent candidates, ideally with a strong background in AI, Robotics, Mathematics, Engineering, or Machine Learning, to apply for this position to turn this funding opportunity into a real project. 
If you are excited about human-robot collaboration and its potential to change the way we manufacture, apply by sending a covering letter outlining your interest and proposed approach (up to 1 page A4) with an accompanying CV to mhanheide@lincoln.ac.uk by close of day on 18th April 2014
More details below (download the official advert here):

Project Title

Facilitating Individualised Collaboration with Robots (FInCoR)

Project Reference


Project Summary

A PhD position is available in the Lincoln Centre for Autonomous Systems Research (L-CAS), a thriving research centre based at the University of Lincoln.

L-CAS is internationally recognised for its applied autonomous systems research, in domains such as manufacturing, agriculture, security, and care. It specialises in the integration of perception, learning, decision-making and control capabilities in autonomous systems such as mobile robots and smart devices.

The Centre benefits from new, modern laboratory facilities, access to state-of-the-art robotic hardware, and offers the successful candidate a strong embedding into existing international research projects with the potential to liaise with highly regarded experts in the field. The candidate will be part of an international and ambitious team, and will benefit from excellent support to produce and disseminate original research contributions.

The PhD position is offered in the area of long-term adaptation and learning for human-robot collaboration. The project will bring together aspects of machine learning, AI and human-robot interaction, all with strong links to real-world application in manufacturing and care.

The successful applicant will be an excellent student with a very good Bachelors or Masters in Computer Science, Electronic Engineering, Mathematics or Physics who is excited about robots and can evidence relevant coding skills (C++/Python/Java/ROS). A background in machine learning, robotics, and/or AI is desirable. The project start date is 1st September 2014.

The FInCoR project will investigate novel ways to facilitate individualised human-robot collaboration through long-term adaptation on the level of joint tasks. This will enable robots to work with human more effectively in scenario such as high value manufacturing and assistive care.

Imagine a robot helping to assemble a car’s dashboard more effectively, knowing that it is working with a left-handed person; or a robot assisting an elderly employee in a car factory who is skilled in fitting a speedometer, but requires a third-hand holding the heavy mounting frame in place. Despite significant progress in human-robot collaboration, today’s robotic systems still lack the ability to adjust to an individual’s needs.

FInCoR will overcome this limitation by developing online, in-situ adaptation, putting the “human in the loop”. It will bring together flexible task representations (eg. Markov Decision Processes), machine learning (eg. reinforcement learning), advanced robot perception (eg. body tracking), and robot control (eg. reactive planning) to make progress from pre-scripted tasks to individualised models. These models account for preferences, abilities, and limitations of each individual human through long-term adaptation. Hence, FInCoR will enable processes known from human-human collaboration, such as two colleagues working together and learning more about each other’s strengths, preferences, and strategies, to take place in human-robot teams. In particular, FInCoR sets out the following objectives:
  • to develop a long-term adaptation framework for task collaboration that is governed by learning signals based on measures of performance, comfort, and ergonomics;
  • to implement the adaptation framework in the de-facto standard for robot software “ROS” to ensure effective dissemination of results and maximise impact;
  • to generate high quality outputs from original research;
  • to explore the potential of individualised adaptation in at least two market domains: high-value manufacturing  and (assistive) care, and 
  • to validate the framework within these domains, with input from international collaboration partners.

Supervisory Team

1. Dr Marc Hanheide, Senior Lecturer, School of Computer Science. http://staff.lincoln.ac.uk/mhanheide

2. Prof Tom Duckett, Professor of Computer Sciences, School of Computer Sciences. http://staff.lincoln.ac.uk/tduckett

Informal Enquiries

For further information on this project please contact Dr Marc Hanheide by email at: mhanheide@lincoln.ac.uk


All Candidates must satisfy the University’s minimum doctoral entry criteria for studentships of an honours degree at Upper Second Class (2:1) or an appropriate Masters degree or equivalent. A minimum IELTS (Academic) score of 7 (or equivalent) is essential for candidates for whom English is not their first language. Funded Studentships are open to both UK/EU students unless otherwise specified.

How to Apply

Please send a covering letter outlining your interest and proposed approach (up to 1 page A4) with an accompanying CV to mhanheide@lincoln.ac.uk by close of day on 18th April 2014.

Candidates will be notified w/c 5th May of the outcome of the process and if invited to interview, these are anticipated to take place w/c 26h May.

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IAS Workshop on Recent Advances in Agricultural Robotics

IAS Workshop on Recent Advances in Agricultural Robotics

My colleagues of L-CAS are organising an IAS Workshop on Recent Advances in Agricultural Robotics in lovely Padova, Italy.  If you are into growing and breeding, and want robots to help you, this is for you.

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Sebastian Wrede presenting at LSoCS Research Seminar

Sebastian Wrede presenting at LSoCS Research Seminar

Everybody in Lincoln is invited to learn about some exciting research in robotics done by my friend and former colleague Sebastian:

Time/Date: 4pm, Tuesday, 1 Oct 2013

Venue: MB1015, Main Admin Building
Speaker:  Dr. Sebastian Wrede, Cognitive Systems Engineering, CoR-Lab, Bielefeld University

Title: Kinesthetic Teaching of Redundant Robots in Task and Configuration Space

Abstract: The recent advent of compliant and kinematically redundant robots poses new research challenges for human-robot interaction. While these robots provide high flexibility for the realization of complex applications, the gained flexibility generates the need for additional modeling steps and the definition of criteria for redundancy resolution constraining the robot’s movement generation. The explicit modeling of such criteria usually require experts to adapt the robot’s movement generation subsystem. A typical way of dealing with this configuration challenge is to utilize kinesthetic teaching and guide the robot to implicitly model the specific constraints in task and configuration space. However, in this presentation we report on experiments showing that current programming-by-demonstration approaches are not efficient for kinesthetic teaching of redundant robots and typical teach-in procedures are too complex for novice users. In order to enable non-experts to master the configuration and programming of a redundant robot in the presence of non-trivial constraints such as confined spaces, the talk presents a new interaction scheme combining kinesthetic teaching and learning within an integrated system architecture. The approach was evaluated in a user study with 49 industrial workers in a medium-sized manufacturing company.  Results show that the interaction concepts implemented on a KUKA Lightweight Robot IV are easy to handle for novice users, demonstrate the feasibility of kinesthetic teaching for implicit constraint modeling in configuration space and yield significantly improved performance for the teach-in of trajectories in task space.

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