Full-day workshop on,
Closing the Loop on Human-Robot Symbiosis: Human/Robot in-the-loop Machine Learning
We are delighted to announce our workshop on Human/Robot in-the-loop Machine Learning, to be held at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), Madrid, Spain, on October 1st, 2018.
A. Shafti, R. Calandra, M.P. Deisenroth, A.A. Faisal
Outside of the classic industrial environments, robots are not expected to operate in the vacuum and isolated from people. Robots are instead performing their tasks close to and in collaboration with or assistance of humans; be this delivery drones, self-driving cars, robot assisted care or collaborative industrial settings. We take inspiration from us humans: We interact in a perception-action loop, where actions have consequences and thus require prediction and reactions. This requires developing a meaningful model of the humans we are interacting with from observations and interactions. Thus, learning is the glue that enables us to link desired outcomes with purposeful actions; and in a digital, data-driven world, machine learning is at the core. We will explore in this workshop, using cross-disciplinary work, how to close-the-loop of modelling and predicting human interactions as well as human-robot interactions.
A major challenge in analyzing such behavior is to discover some underlying simplicity in a complex and highly variable stream of behavioral actions. The gain of such an analysis is that the underlying simplicity is often a reflection of the mechanism driving behavior. We believe that advanced statistical and probabilistic methods can be used to analyse the unconstrained natural statistics of behaviour. Similar methods need to be applied to robotic systems, understanding their behaviour in conjunction and interaction with the human, to ensure compatibility and complementarity within the human-robot interaction, which is in this case, happening as a closed-loop.
This workshop aims to create a forum for experts in robotics, machine learning, human behaviour analytics, computational neuroscientists, and all relevant stakeholders within academia and industry, on how the current research within these different fields can be brought together to create a new paradigm for human/robot in-the-loop machine learning. To this end, speakers have been selected with care, bringing in top experts and representatives from the above in a unique environment for joint discussions and understanding across the different platforms of research. We hope that this, in parallel with our plans to organise a special issue on the same topic with RA-L, will lead to a new community of cross-disciplinary research, with further workshops of the same topic organised for future and further collaboration.
General action-perception loop covered within this workshop:
- Peter Battaglia, Google DeepMind
- Aude Billard, École Polytechnique Fédérale de Lausanne (EPFL)
- Haitham Bou-Ammar, PROWLER.io
- Daniel Braun, University of Ulm (UULM)
- Sylvain Calinon, École Polytechnique Fédérale de Lausanne (EPFL)
- Ohad Dan (representing Yonatan Loewenstein’s lab), The Hebrew University of Jerusalem
- Aaron Dollar, Yale University
- Anca Dragan, University of California, Berkeley
- Moritz Grosse-Wentrup, Max Planck Institute for Intelligent Systems
- Sami Haddadin, Leibniz Universität hannover
- Sethu Vijayakumar, University of Edinburgh [tentative]
- Tamar Makin, University College London
- Franzi Meier, Max Planck Institute for Intelligent Systems
- Igor Mordatch, OpenAI
- Gerhard Neumann, University of Lincoln
- Domenico Prattichizzo, Università di Siena
- Francisco Valero-Cuevas, University of Southern California
- Jeremy Wyatt, University of Birmingham
IROS 2018 is expected to cover the main topics within robotics research; as highlighted on the conference website: Human-robot interaction, humanoids, social robots, autonomous systems and, intelligent perception, as well as social aspects of robotics. While researchers in these fields are incorporating machine learning methods in their work, there is no specific session within IROS that is focused on how machine learning can enhance robotics research. In particular, our workshop’s focus on human and robot in-the-loop techniques, makes the discussions interesting to both the robotics and the machine learning community. Our speakers are top researchers from these fields, providing the opportunity for fruitful discussions on how the communities can come together for better collaboration and research output.
Stay tuned! We’ll soon update this page with further details on topics covered by our speakers, and a call for contributions in the relevant topics of interest.
Topics of Interest:
- Machine learning
- Human learning
- Reinforcement learning
- Computational Neuroscience
- Multi-agent systems
- Multi-agent reinforcement learning
- Deep reinforcement learning
- Human-in-the-loop machine learning
- Robot-in-the-loop machine learning
- Human-robot interaction
- Human-robot collaboration
For more information and enquiries, contact Dr. Ali Shafti: firstname.lastname@example.org
We have been endorsed by the following IEEE RAS Technical Committees: