[robotics-worldwide] [meetings] Call for Participation: ICRA 2018 Workshop on Machine Learning in Planning and Control of Robot Motion

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[robotics-worldwide] [meetings] Call for Participation: ICRA 2018 Workshop on Machine Learning in Planning and Control of Robot Motion

Lewis Chiang
** We have received many exciting paper and poster submissions (final
program out shortly), we look forward to see you at ICRA 2018**

*Apologies for cross postings*

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Third Machine Learning in Planning and Control of Robot Motion Workshop at
ICRA 2018May 21, 2018 -- Brisbane, Australia
Website: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.cs.unm.edu_amprg_Workshops_MLPC18_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=iuc98rcVH10KNm-mdwjklufZ7TDWiONXsV6HIfc8RhI&s=RpryT30tv0isCLCwxLYDun22uDqNM-vAU9-Z3YY7lEs&e=
Contact: [hidden email]
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Overview

The workshop aims to spark vibrant discussion with talks from invited
speakers, presentations from authors of accepted papers, and a poster
session. Because machine learning methods are often heuristic, issues such
as safety and performance are critical. Also, learning-based questions such
as problem learnability, knowledge transfer among robots, knowledge
generalization, long-term autonomy, task formulation, demonstration, role
of simulation, and methods for feature selection define problem solvability.
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Intended audience

- Motion planners with interests in learning and planning for changing
agents, environment, or both
- Reinforcement learning and machine learning communities that develop
novel learning methods for autonomous agents
- Multi-agent researchers
- Controls community focused on controlling physical systems
- Robotics community

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Topics of interest

Topics include, but are not limited to:
- Task representation and classification
- Planning for complex and high dimensional environments
- Smart sampling techniques for motion planning
- Learning feature selection
- Methods for incorporating learning into planning
- Reinforcement learning for robotics and dynamical systems
- Transfer of learning and motion plans, knowledge and experience sharing
among the agents
- Policy selection: exploration versus exploitation, methods for safe
exploration
- Methods for creating motion plans that meet dynamical constraints
- Task planning and learning under uncertainty and disturbance
- Motion planning for system stability
- Adaptable heuristics for efficient motion plans
- Motion generalization - methods that learn subset of motion and produce
plans with higher range of motions
- Motion planning for multi-agent systems and fleets

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Invited keynote speakers

Dana Nau, University of Maryland
David Hsu, National University of Singapore
Oliver Brock, Technische Universit├Ąt Berlin

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Important dates

Acceptance notification: April 23, 2018
Camera-ready deadline: May 15, 2018
Workshop: May 21, 2018

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Location

The Brisbane Convention & Exhibition Centre
Room M4 (Mezzanine Level)

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Organizers

- Aleksandra Faust, Google Brain, USA
- Tsz-Chiu Au, Ulsan National Institute of Science and Technology, South
Korea
- James Davidson, Google Brain, USA
- Hanna Kurniawati, University of Queensland, Australia
- Lydia Tapia, University of New Mexico, USA
- Hao-Tien Lewis Chiang, University of New Mexico, USA
--
/*********************************************************
Hao-Tien (Lewis) Chiang

PhD student, Department of Computer Science
University of New Mexico
Email: [hidden email], Url: https://urldefense.proofpoint.com/v2/url?u=https-3A__www.cs.unm.edu_amprg&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=iuc98rcVH10KNm-mdwjklufZ7TDWiONXsV6HIfc8RhI&s=hQBYnhYUbJ3dpyZ0RV-WFiWB1K9KKxaJg7ySaWRlcpA&e=
*********************************************************/
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