Machine Learning for Motion Planning

Call for Participation

Workshop Website: https://sites.google.com/utexas.edu/mlmp-icra2021 

Submission Site: https://easychair.org/conferences/?conf=mlmp2021 

Submission Deadline: April 30 2021

Motion planning is one of the core problems in robotics with applications ranging from navigation to manipulation in complex cluttered environments. It has a long history of research with methods promising full to probabilistic completeness and optimality guarantees. However, challenges still exist when classical motion planners face real-world robotics problems in high dimensional or highly constrained workspaces. The community continues to develop new strategies to overcome limitations associated with these methods, which include computational and memory burdens, planning representation, and the curse of dimensionality.

In contrast, recent advancements in machine learning have opened up new perspectives for roboticists to look at the motion planning problem: bottlenecks of classical motion planners can be addressed in a data-driven manner; classical planners can go beyond the geometric sense and enable orthogonal planning capabilities, such as planning with visual or semantic input, or in a socially-compliant manner. 

The objective of this workshop is to bring the two research communities under one forum to discuss the lessons learned, open questions, and future directions of machine learning for motion planning. We aim to identify the gaps and formalize the merging points between the two schools of methodologies, e.g. workspace representation, sample generation, collision checking, cost definition, and answer the questions of why, where, and how to apply machine learning for motion planning. 

Papers of up to two-six pages are sought in the following topic areas:

Topics of interest: 

• Data-driven approaches to motion planning
• Learning-based adaptive sampling methods
• Learning models for planning and control
• Imitation learning for planning and control
• Learning generalizable and transferable planning models
• Representation learning for planning
• Learning-based collision detection, edge selection, and pruning techniques, and related topics
• Data-efficiency in data-driven techniques to planning
• Formal guarantees to machine learning-based planning methods
• Learning methods for hierarchical planning such task and motion planning, multi-model motion planning, and related topics
• Active/lifelong/continual learning methods for planning and related topics

Organizers: 

1. Xuesu Xiao, Department of Computer Science, The University of Texas at Austin, 2317 Speedway, Austin, TX 78712, USA, Phone: +1 (512) 471-9765, Email: xiao@cs.utexas.edu, URL: https://www.cs.utexas.edu/~xiao/ (Primary Contact)
2. Ahmed H. Qureshi, Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA, Phone: +1 (858) 349-8122, Email: a1quresh@ucsd.edu, URL: https://qureshiahmed.github.io/ 
3. Anastasiia Varava, School of Computer Science and Communication, KTH Royal Institute of Technology,  SE-100 44 Stockholm, Sweden, Email: varava@kth.se, URL: https://anvarava.github.io/ 
4. Michael Everett, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Ave, 31-235C, Cambridge, MA 02139, Phone: +1 (734) 476-2051, Email: mfe@mit.edu, URL: http://mfe.mit.edu 
5. Michael C. Yip, Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA, Phone: +1 (858) 822-4778, Email: yip@ucsd.edu, URL: https://yip.eng.ucsd.edu/  
6. Peter Stone, Department of Computer Science, The University of Texas at Austin, 2317 Speedway, Austin, TX 78712, USA, Phone: +1 (512) 471-9796, Email: pstone@cs.utexas.edu, URL: https://www.cs.utexas.edu/~pstone/ 

Steering Committee:

1. Danica Kragic, KTH Royal Institute of Technology, Sweden. Email:dani@kth.se
2. Jonathan How, Massachusetts Institute of Technology (MIT), USA. Email: 
3. Jan Peters, Technische Universität Darmstadt, Germany. Email: peters@tu-darmstadt.de
4. Howie Choset, Carnegie Mellon University (CMU), USA. Email: choset@cmu.edu 
5. Steven LaValle, University of Oulu, Finland. Email: steven.lavalle@oulu.fi
6. Lydia Kavraki, Rice University, USA. Email: kavraki@rice.edu
7. Seth Hutchinson, GeorgiaTech, USA. Email: seth@gatech.edu
8. Aude Billard,  École polytechnique fédérale de Lausanne (EPFL), aude.billard@epfl.ch
9. Aleksandra Faust, Google Brain Research, faust@google.com 

Invited Speakers:

1. Sertac Karaman, Massachusetts Institute of Technology (MIT). Email: sertac@mit.edu
2. Raquel Urtasun, University of Toronto & Uber ATG. Email:urtasun@cs.toronto.edu
3. Marc Toussaint, Technische Universität Berlin. Email: toussaint@tu-berlin.de
4. Anca Dragan, University of California Berkeley, USA. Email: anca@berkeley.edu

Preliminary Schedule: 

09:00 – 09:05             Opening Remarks 

09:05 – 09:35              Invited Talk 1

09:35 – 09:55             Spotlight Presentations 

09:55 – 10:00              Coffee Break

10:00 – 10:30              Invited Talk 2

10:30 – 10:55              Spotlight Presentations 

10:55 – 11:00              Coffee Break

11:00 – 11:30              Invited Talk 3

11:30 – 12:00              Spotlight Presentations 

12:00 – 13:00              Lunch

13:00 – 13:30              Invited Talk 4

13:30 – 13:55              Spotlight Presentations

13:55 – 14:00             Coffee Break

14:00 – 14:30              Invited Talk 5

14:30 – 15:45              Breakout Sessions

15:45 – 15:55              Reconvene and Report

15:55 – 16:55              Panel Discussion

16:55 – 17:00              Awards and Closing Remarks 

Technical Committee Endorsement:

1. IEEE RAS Technical Committee on Algorithms for Planning and Control of Robot Motion
2. IEEE-RAS Technical Committee on Robot Learning

For questions, please contact

Dr. Xuesu Xiao

Department of Computer Science

The University of Texas at Austin

2317 Speedway, Austin, Texas 78712-1757 USA

+1 (512) 471-9765

xiao@cs.utexas.edu

https://www.cs.utexas.edu/~xiao/