Important Details

• Workshop date and time: May 13th or 17th, 2024 in Yokohama, Japan at ICRA’24.
• Registration link:
• Submission deadline:
• Submission link:
• Paper format: IEEE RAS format
• Recommended length: 2-4 pages
• Review process: single-blind
• Notification of acceptance:

Guaranteeing safety is crucial for the effective deployment of robots, in general autonomous systems. For realizing the fully reliable system, all the components in perception, navigation, and control must incorporate safety considerations. 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. Many 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.

Recent progress in machine learning allows learning robot dynamics and environment models from sensory data. For example, Gaussian Process regression and Koopman operator theory have shown promise in learning dynamics models. Similarly, deep neural network models have enabled impressive results in 3D reconstruction from visual data. Despite their empirical success, these works usually do not provide theoretical guarantees for safety and stability. Recent works in Bayesian deep learning have allowed us to associate uncertainty with the learned predictions, making it possible to use safe control approaches in machine learning enabled systems. However, the trustworthiness of uncertainty estimates of Bayesian deep learning models still remains an open problem.

This workshop will bring together experts from multiple communities – robotics, control theory, computer vision, and machine learning – to address challenges in safe and stable robot control with learned motion and environment models.

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:

1. Control-theoretic techniques for safe task execution, including control barrier functions, reachability analysis, model predictive control, reference governor control, contraction theory.
2. Machine learning techniques for safe task execution, including model-based reinforcement learning, robot dynamics model learning and system identification, learning 3-D environment shape and dynamics.

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:

1. What is the approach for estimating robot dynamics or environment models?
2. How are the estimation errors quantified and used in verifying safety or stability?
3. What is the key contribution of this approach?
4. What is a key research problem that the community should address in future work and whose resolution will significantly impact your work?

Spotlight Video

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
07:00-07:30 AM	10:00-10:30 AM
07:30-08:00 AM	10:30-11:00 AM
08:00-08:30 AM	11:00-11:30 AM
08:30-09:00 AM	11:30-12:00 PM	Coffee break
09:00-09:30 AM	12:00-12:30 PM
09:30-10:00 AM	12:30-01:00 PM
10:00-10:30 AM	01:00-01:30 PM
10:30-12:00 PM	01:30-03:00 PM	Lunch break
12:00-12:30 PM	03:00-03:30 PM
12:30-01:00 PM	03:30-04:00 PM
01:00-01:30 PM	04:00-04:30 PM	Coffee break and Interactive session
01:30-02:00 PM	04:30-05:00 PM	Interactive session for accepted papers
02:00-02:30 PM	05:00-05:30 PM
02:30-03:00 PM	05:30-06:00 PM
03:00-03:30 PM	06:00-06:30 PM	Discussion and closing remarks

Titles and Abstracts for the talks

Contact

Should you have any questions, please do not hesitate to contact the organizers Vikas Dhiman (vikas.dhiman@maine.edu) or Shumon Koga (shumon.sdr@gmail.com). Please include ICRA 2024 Workshop in the subject of the email.