Guaranteeing safety is crucial for the effective deployment of robots. Control theory research has established techniques with theoretical safety and stability guarantees based on model predictive control, reference governor design, Hamilton-Jacobi reachability, control Lyapunov and barrier functions, and contraction theory. Similarly, formal methods techniques based on SMT solvers and hybrid system verification have been used to guarantee safety in systems. Existing techniques, however, predominantly assume that the robot motion dynamics and safety constraints are precisely known in advance. This assumption cannot be satisfied in the unstructured and dynamic real-world conditions. For example, an aerial vehicle aiding in disaster response must operate in an unpredictable environment subject to extreme disturbances. Similarly, a walking robot providing last-mile delivery has to traverse changing terrain while negotiating pedestrian traffic.
With recent progress in machine learning we can learn robot dynamics or environment models from sensory data. Gaussian Process regression and Koopman Operator theory have shown promise in estimating robot dynamics models. Deep neural network models have enabled impressive results in 3D reconstruction from visual data. Although empirically impressive, these machine learning techniques, however, do not provide guarantees for safety.
This workshop seeks to bring together experts from multiple communities – robotics, control theory and machine learning – and highlight the cutting-edge research in their intersection. We will feature talks from both the fields with an emphasis on safe robot control in uncertain environments.
We invite submissions from a broad range of topics that investigate the formal safety of robots when dealing with uncertainty introduced when the robot dynamics models are learned or the environment state is estimated. We provide a non-exhaustive list of topics that might be of interest to the target audience for this workshop:
Priority will be given to papers that bridge the gap between the two areas to provide safety and stability guarantees for systems with learned motion and environment dynamics. The review committee will judge the contributions based on the following questions:
Accepted papers will be required to submit a spotlight video that provides a demo of the proposed approach and answers the four key questions related to our workshop. In the demo part of the video, the authors are encouraged to demonstrate the operation of their system (either real or simulated) in a safety critical scenario. The spotlight videos will be presented during the time allocated for the poster session.
|Time (PST, GMT-07)||Time (EST, GMT-04)||Topic|
|06:45-07:00 AM||09:45-10:00 AM||Registration, welcome, and opening remarks|
|07:00-07:30 AM||10:00-10:30 AM||Invited talk: Melanie N. Zeilinger, ETH Zurich|
|07:30-08:00 AM||10:30-11:00 AM||Invited talk: Dimitra Panagou, University of Michigan|
|08:00-08:30 AM||11:00-11:30 AM||Virtual poster session|
|08:30-09:00 AM||11:30-12:00 PM||Coffee break|
|09:00-09:30 AM||12:00-12:30 PM||Virtual poster session|
|09:30-10:00 AM||12:30-01:00 PM||Invited talk: Kelsey Allen, Massachusetts Institute of Technology|
|10:00-10:30 AM||01:00-01:30 PM||Invited talk: Sergey Levine, UC Berkeley|
|10:30-12:00 PM||01:30-03:00 PM||Lunch break|
|12:00-12:30 PM||03:00-03:30 PM||Invited talk: Gaurav Sukhatme, University of Southern California|
|12:30-01:00 PM||03:30-04:00 PM||Invited talk: Claire J. Tomlin, UC Berkeley|
|01:00-01:30 PM||04:00-04:30 PM||Coffee break and Poster session|
|01:30-02:00 PM||04:30-05:00 PM||Virtual poster session|
|02:00-02:30 PM||05:00-05:30 PM||Invited talk: Qi (Rose) Yu, UC San Diego|
|02:30-03:00 PM||05:30-06:00 PM||Invited talk: Aaron Ames, California Institute of Technology|
|03:00-03:30 PM||06:00-06:30 PM||Discussion and closing remarks|
Should you have any questions, please do not hesitate to contact the organizers Vikas Dhiman (email@example.com) or Shumon Koga (firstname.lastname@example.org). Please include
ICRA 2021 Workshop in the subject of the email.