Call for Papers

Control and decision systems are becoming a ubiquitous part of our daily lives, ranging from serving advertisements on the internet to controlling autonomous physical systems such as industrial equipment or robots. While these systems have shown the potential for significantly improving quality of life and industrial efficiency, the impact of the decisions made by these systems can also cause significant damages. For example, an online retailer recommending dangerous products, a social media platform which spreads misinformation, or a household robot/autonomous car which collides with surrounding humans/objects can all cause significant direct harm to society.

These undesirable behaviors not only can be dangerous, but also lead to significant inefficiencies when deploying learning-based agents in the real world. This motivates developing algorithms for learning-based control which can reason about uncertainty and constraints in the environment to explicitly avoid undesirable behaviors. We hope that this workshop will serve to connect researchers from a variety of disciplines including machine learning, control theory, AI safety, operations research, robotics, and formal methods to help tackle these challenges.

The purpose of this workshop is to bring together researchers from both industry and academia working on the full spectrum from theoretical work on safety guarantees for learning-based control systems to practical systems for effectively deploying these systems in safety critical settings. A principal goal is to study how autonomous agents can (1) effectively identify unsafe behaviors and (2) learn how to avoid them both in theory and in practice. To this end, we welcome any submissions focused on safety and robustness for reinforcement learning and control, but particularly encourage submissions on the following topics:

• Safe and Efficient Exploration: How do we explore an uncertain environment while avoiding undesirable or constraint violating states/actions and avoiding resets?

• Specifying Undesirable Behaviors: How do we convey undesirable behaviors to an autonomous agent scalably and efficiently so that it can learn new tasks safely?

• Off-Policy Evaluation: How can we evaluate performance before execution in the environment?

• Model-Based Controller Design + Data: How can we synthesize ideas from control theory/formal logic and machine learning to design provably safe controllers for systems with uncertain dynamics?

• Offline RL/Control: How can we leverage offline data to learn robust controllers/policies before interacting with the environment?

• Active and Human-in-the-loop Learning: How can we leverage human interactions to enable better exploration strategies and more robust policies?

• Scalability and Safety: How can we balance tractability and scalability with robustness to uncertainty in reward functions and system dynamics?

Submissions should be 4 page extended abstracts (4 pages + additional pages for references and supplementary material) in the NeurIPS 2021 format and submitted through CMT here. Authors may also submit up to 100 MB in supplementary material such as appendices, proofs, code, or additional experimental details. All submissions, including supplementary material, must be anonymized. Accepted submissions will be presented in the form of posters or contributed talks. We will not accept submissions which are already published in machine learning conferences or journals (NeurIPS, ICML, ICLR, JMLR, etc...) but are happy to accept submissions which are under review at any venue.

We also encourage you to check out a related NeurIPS 2021 workshop on deployable decision making in embodied systems.

Schedule (December 13, 2021, 8 AM - 4 PM)

ACCEPTED PAPERS