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Mark Mon-Williams

dblp:18/3029 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0001-7595-8545ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 2Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 50% Motion planning and robot control · 50%
Computer graphics and multimedia
2 papers
Virtual and augmented reality · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
trajectory optimization
0.412020
Human-like Planning for Reaching in Cluttered Environments · ICRA 2020
Virtual and augmented reality
immersive interaction
0.112020
Human-like Planning for Reaching in Cluttered Environments · ICRA 2020
Virtual and augmented reality
human factors
0.011996
What does virtual reality NEED?: human factors issues in the design of three-dimensional computer environments · Int. J. Hum. Comput. Stud. 1996
Virtual and augmented reality
virtual reality
0.011996
What does virtual reality NEED?: human factors issues in the design of three-dimensional computer environments · Int. J. Hum. Comput. Stud. 1996

Methods — techniques the papers use, named apart from their topics

virtual reality · 0.9trajectory optimization · 0.9decision classifiers · 0.9
YearPublicationVenuePosition
2020 Human-like Planning for Reaching in Cluttered Environments
abstract
Humans, in comparison to robots, are remarkably adept at reaching for objects in cluttered environments. The best existing robot planners are based on random sampling of configuration space- which becomes excessively high-dimensional with large number of objects. Consequently, most planners often fail to efficiently find object manipulation plans in such environments. We addressed this problem by identifying high-level manipulation plans in humans, and transferring these skills to robot planners. We used virtual reality to capture human participants reaching for a target object on a tabletop cluttered with obstacles. From this, we devised a qualitative representation of the task space to abstract the decision making, irrespective of the number of obstacles. Based on this representation, human demonstrations were segmented and used to train decision classifiers. Using these classifiers, our planner produced a list of waypoints in task space. These waypoints provided a high-level plan, which could be transferred to an arbitrary robot model and used to initialise a local trajectory optimiser. We evaluated this approach through testing on unseen human VR data, a physics-based robot simulation, and a real robot (dataset and code are publicly available1). We found that the human-like planner outperformed a state-of-the-art standard trajectory optimisation algorithm, and was able to generate effective strategies for rapid planning- irrespective of the number of obstacles in the environment.
Mohamed Hasan, Matthew Warburton, Wisdom C. Agboh, Mehmet Remzi Dogar, Matteo Leonetti, He Wang 0002, Faisal Mushtaq, Mark Mon-Williams, Anthony G. Cohn 0001
ICRA8
2006 A Cooperative Control Scheme for Robotic Rehabilitation of Arm Impairment after Stroke
abstract
Recovery of arm movement after stroke partly relates to quantity of functionally relevant practice. Physical therapies involve careful guidance of the paretic arm through therapeutic exercise to allow relearning of joint co-ordinations needed for functional arm movement. A robotic device that helps deliver more rehabilitation treatment would accelerate recovery of arm weakness. This paper describes the development of a cooperative controller for a robotic system (iPAM) that is being developed which provides intelligent, interactive, safe movement treatment to help recovery of arm weakness after stroke
Peter Culmer, Andrew E. Jackson, Martin Levesley, Robert Richardson 0001, J. A. Cozens, Mark Mon-Williams, B. B. Bhakta
IROS6
1996 What does virtual reality NEED?: human factors issues in the design of three-dimensional computer environments
John P. Wann, Mark Mon-Williams
Int. J. Hum. Comput. Stud.2