Research Areas

Multi-Agent Coordination from Specifications

We developed techniques for coordinating large teams of heterogenous agents from temporal logic specifications. This work leverages a fragment of signal temporal logic that is chosen to be amenable to large numbers of agents. The result is a scalable framework that has been demonstrated on very large numbers of agents in simulation, as well as large teams of real robots.

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Scalable and Robust Algorithms for Task-Based Coordination From High-Level Specifications (ScRATCHeS)

Composable RL from Specifications

This work focuses on guaranteed reinforcement learning, using specifications expressed in Boolean and temporal logic. The goal is to train policies on simple, atomic tasks that can be composed at runtime with no additional training. Specifically, we focus on including both safety and reachability requirements.

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Safety-Aware Task Composition for Discrete and Continuous Reinforcement Learning

Safe Control with Machine Learning in the Loop

Human-Robot Tasking via Formal Specifications

This work focuses on advancing the ability of a non-expert user to task an autonomous system. While many planning formalisms exists for expressing high-level objectives, it remains difficult for most end-users to specify their goals in those formalisms. We attempt to understand how humans interpret formal languages and how we can make them more interpretable to humans. Likewise, we attempt to develop methods for eliciting expectations and preference from users that can be incorporated into planners.

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STL: Surprisingly Tricky Logic (for System Validation)