Consider one or more AI agents performing daily living tasks in collaboration with a human they have not worked with before. Figure 1 shows snapshots of a motivating scenario in which two AI agents (male, blue shirt; female, red dress) and a human agent (female, green top) are preparing breakfast and setting up a workstation. The agents (AI, human) have a limited view of the environment and do not communicate with each other, although each of them is aware of the state of the domain, including the location of teammates and the outcomes of their actions (e.g., change in location of an object moved by a teammate). The AI agents have to reason with different descriptions of domain knowledge and uncertainty that include qualitative statements ("eggs are usually in the fridge") and quantitative measures of uncertainty ("I am 90% sure I saw the eggs on the kitchen table"), adapting their actions to changes in the domain and teammates' behavior. These characteristics correspond to Ad Hoc T eamwork (AHT), which requires cooperation "on the fly" without prior coordination [1]; many practical problems such as disaster rescue are AHT problems. The state of the art in AHT has moved from using preset protocols that define specific actions to be performed in specific states, to methods that use a long history of prior experiences to build a deep network model of the behavior of other agents (or agent types) and optimize the ad hoc agent's behavior [2]. However, it is difficult to gather large datasets of different situations in complex domains. Also, these methods are opaque and make it difficult to revise the existing models over time. In a departure from existing work, we design an architecture for AHT that bridges knowledge-based and data-driven reasoning and learning, enabling an ad hoc agent to: Leverage the ability of a Large Language Model (LLM) to anticipate future high-level tasks to be completed, revising and adapting the LLM's output to domain-specific knowledge and experience; Perform non-monotonic logical reasoning with prior commonsense domain knowledge at different abstractions, and learned models predicting the behavior of other agents, toward achieving current and anticipated tasks as joint goals; and Rapidly identify the need for, learn, and revise the models predicting the behavior of each teammate to facilitate scalable collaboration in complex domains.

Ad hoc teamwork refers to the problem of enabling an agent to collaborate with teammates without prior coordination. Data-driven methods represent the state of the art in ad hoc teamwork. They use a large labeled dataset of prior observations to model the behavior of other agent types and to determine the ad hoc agent's behavior. These methods are computationally expensive, lack transparency, and make it difficult to adapt to previously unseen changes, e.g., in team composition. Our recent work introduced an architecture that determined an ad hoc agent's behavior based on non-monotonic logical reasoning with prior commonsense domain knowledge and predictive models of other agents' behavior that were learned from limited examples. In this paper, we substantially expand the architecture's capabilities to support: (a) online selection, adaptation, and learning of the models that predict the other agents' behavior; and (b) collaboration with teammates in the presence of partial observability and limited communication. We illustrate and experimentally evaluate the capabilities of our architecture in two simulated multiagent benchmark domains for ad hoc teamwork: Fort Attack and Half Field Offense. We show that the performance of our architecture is comparable or better than state of the art data-driven baselines in both simple and complex scenarios, particularly in the presence of limited training data, partial observability, and changes in team composition.

Behavior prediction remains one of the most challenging tasks in the autonomous vehicle (AV) software stack. Forecasting the future trajectories of nearby agents plays a critical role in ensuring road safety, as it equips AVs with the necessary information to plan safe routes of travel. However, these prediction models are data-driven and trained on data collected in real life that may not represent the full range of scenarios an AV can encounter. Hence, it is important that these prediction models are extensively tested in various test scenarios involving interactive behaviors prior to deployment. To support this need, we present a simulation-based testing platform which supports (1) intuitive scenario modeling with a probabilistic programming language called Scenic, (2) specifying a multi-objective evaluation metric with a partial priority ordering, (3) falsification of the provided metric, and (4) parallelization of simulations for scalable testing. As a part of the platform, we provide a library of 25 Scenic programs that model challenging test scenarios involving interactive traffic participant behaviors. We demonstrate the effectiveness and the scalability of our platform by testing a trained behavior prediction model and searching for failure scenarios.

Driving in a dynamic environment that consists of other actors is inherently a risky task as each actor influences the driving decision and may significantly limit the number of choices in terms of navigation and safety plan. The risk encountered by the Ego actor depends on the driving scenario and the uncertainty associated with predicting the future trajectories of the other actors in the driving scenario. However, not all objects pose a similar risk. Depending on the object's type, trajectory, position, and the associated uncertainty with these quantities; some objects pose a much higher risk than others. The higher the risk associated with an actor, the more attention must be directed towards that actor in terms of resources and safety planning. In this paper, we propose a novel risk metric to calculate the importance of each actor in the world and demonstrate its usefulness through a case study.

To achieve seamless human-robot interactions, robots need to intimately reason about complex interaction dynamics and future human behaviors within their motion planning process. However, there is a disconnect between state-of-the-art neural network-based human behavior models and robot motion planners -- either the behavior models are limited in their consideration of downstream planning or a simplified behavior model is used to ensure tractability of the planning problem. In this work, we present a framework that fuses together the interpretability and flexibility of trajectory optimization (TO) with the predictive power of state-of-the-art human trajectory prediction models. In particular, we leverage gradient information from data-driven prediction models to explicitly reason about human-robot interaction dynamics within a gradient-based TO problem. We demonstrate the efficacy of our approach in a multi-agent scenario whereby a robot is required to safely and efficiently navigate through a crowd of up to ten pedestrians. We compare against a variety of planning methods, and show that by explicitly accounting for interaction dynamics within the planner, our method offers safer and more efficient behaviors, even yielding proactive and nuanced behaviors such as waiting for a pedestrian to pass before moving.