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Learning Shared Safety Constraints from Multi-task Demonstrations
Konwoo Kim · Gokul Swamy · ZUXIN LIU · DING ZHAO · Sanjiban Choudhury · Steven Wu

Tue Dec 12 08:45 AM -- 10:45 AM (PST) @ Great Hall & Hall B1+B2 #1501

Regardless of the particular task we want to perform in an environment, there are often shared safety constraints we want our agents to respect. For example, regardless of whether it is making a sandwich or clearing the table, a kitchen robot should not break a plate. Manually specifying such a constraint can be both time-consuming and error-prone. We show how to learn constraints from expert demonstrations of safe task completion by extending inverse reinforcement learning (IRL) techniques to the space of constraints. Intuitively, we learn constraints that forbid highly rewarding behavior that the expert could have taken but chose not to. Unfortunately, the constraint learning problem is rather ill-posed and typically leads to overly conservative constraints that forbid all behavior that the expert did not take. We counter this by leveraging diverse demonstrations that naturally occur in multi-task setting to learn a tighter set of constraints. We validate our method with simulation experiments on high-dimensional continuous control tasks.

Author Information

Konwoo Kim
Gokul Swamy (Carnegie Mellon University)
ZUXIN LIU (Carnegie Mellon University)
DING ZHAO (Carnegie Mellon University)
Sanjiban Choudhury (Cornell University)
Steven Wu (Carnegie Mellon University)
Steven Wu

I am an Assistant Professor in the School of Computer Science at Carnegie Mellon University. My broad research interests are in algorithms and machine learning. These days I am excited about: - Foundations of responsible AI, with emphasis on privacy and fairness considerations. - Interactive learning, including contextual bandits and reinforcement learning, and its interactions with causal inference and econometrics. - Economic aspects of machine learning, with a focus on learning in the presence of strategic agents.

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