Jim Mainprice

Autonomous Motion

Office:
Max-Planck-Ring 4
72076 Tübingen
Germany

Overview
Publications

Jim Mainprice is leading a research group at the University of Stuttgart, Germany. His research interests include motion planning, machine learning and human-robot interaction. He holds a M.Sc. from Polytech'Montpellier, France, and a Ph.D. from the University of Toulouse, France, which he received in 2009 and 2012 respectively. While completing his Ph.D. at LAAS-CNRS, he took part in the EU FP7 projects Dexmart and Saphari. From January 2013 to December 2014, he was a postdoctoral researcher in the Autonomous Robotic Collaboration Lab at the Worcester Polytechnic Institute (WPI) located in Massachusetts, USA, where he participated in the DARPA Robotics Challenge as a member of the DRCHubo team. Since January 2015, he is affiliated with the Autonomous Motion Department (AMD) of the Max Planck Institute for Intelligent Systems in Tübingen, Germany, and he leads the Humans to Robots Motion (HRM) research group at the University of Stuttgart since April 2017.

4 results (View BibTeX file of all listed publications)

2020

Learning Sensory-Motor Associations from Demonstration

Berenz, V., Bjelic, A., Herath, L., Mainprice, J.

29th IEEE International Conference on Robot and Human Interactive Communication (Ro-Man 2020), August 2020 (conference) Accepted

Abstract

We propose a method which generates reactive robot behavior learned from human demonstration. In order to do so, we use the Playful programming language which is based on the reactive programming paradigm. This allows us to represent the learned behavior as a set of associations between sensor and motor primitives in a human readable script. Distinguishing between sensor and motor primitives introduces a supplementary level of granularity and more importantly enforces feedback, increasing adaptability and robustness. As the experimental section shows, useful behaviors may be learned from a single demonstration covering a very limited portion of the task space.

[BibTex]

2020

am Berenz, V., Bjelic, A., Herath, L., Mainprice, J. Learning Sensory-Motor Associations from Demonstration 29th IEEE International Conference on Robot and Human Interactive Communication (Ro-Man 2020), August 2020 (conference) Accepted

[BibTex]

2018

Real-time Perception meets Reactive Motion Generation

(Best Systems Paper Finalists - Amazon Robotics Best Paper Awards in Manipulation)

Kappler, D., Meier, F., Issac, J., Mainprice, J., Garcia Cifuentes, C., Wüthrich, M., Berenz, V., Schaal, S., Ratliff, N., Bohg, J.

IEEE Robotics and Automation Letters, 3(3):1864-1871, July 2018 (article)

Abstract

We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. Our approach emphasizes the importance of continuous, real-time perception and its tight integration with reactive motion generation methods. We present a fully integrated system where real-time object and robot tracking as well as ambient world modeling provides the necessary input to feedback controllers and continuous motion optimizers. Specifically, they provide attractive and repulsive potentials based on which the controllers and motion optimizer can online compute movement policies at different time intervals. We extensively evaluate the proposed system on a real robotic platform in four scenarios that exhibit either challenging workspace geometry or a dynamic environment. We compare the proposed integrated system with a more traditional sense-plan-act approach that is still widely used. In 333 experiments, we show the robustness and accuracy of the proposed system.

arxiv video video link (url) DOI Project Page [BibTex]