@conference {5291, title = {Incorporating Rich Social Interactions Into MDPs}, booktitle = {2022 IEEE International Conference on Robotics and Automation (ICRA)2022 International Conference on Robotics and Automation (ICRA)}, year = {2022}, month = {05/2022}, address = {Philadelphia, PA, USA}, abstract = {

Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions originating from microsociology can be formalized by extending a nested MDP where agents reason about arbitrary functions of each other\&$\#$x0027;s rewards. This extended Social MDP allows us to encode the five basic interactions that underlie microsociology: cooperation, conflict, coercion, competition, and exchange. The result is a robotic agent capable of executing social interactions in new environments with no interaction-specific training; like humans it can engage socially in novel ways even without a single example of that social interaction. Moreover, the estimations of these Social MDPs align closely with the judge-ments of humans when considering which social interaction is taking place in an environment. This method both sheds light on the nature of social interactions, by providing concrete mathematical definitions, and brings rich social interactions into a mathematical framework that has proven to be natural for robotics.

}, doi = {10.1109/ICRA46639.2022.9811991}, url = {https://ieeexplore.ieee.org/document/9811991/}, author = {Tejwani, Ravi and Kuo, Yen-Ling and Shu, Tianmin and Stankovits, Bennett and Gutfreund, Dan and Tenenbaum, Joshua B. and Katz, Boris and Barbu, Andrei} } @conference {5306, title = {Trajectory Prediction with Linguistic Representations}, booktitle = {2022 IEEE International Conference on Robotics and Automation (ICRA)}, year = {2022}, address = {Philadelphia, PA, USA}, abstract = {

Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory prediction model that uses linguistic intermediate representations to forecast trajectories, and is trained using trajectory samples with partially-annotated captions. The model learns the meaning of each of the words without direct per-word supervision. At inference time, it generates a linguistic description of trajectories which captures maneuvers and interactions over an extended time interval. This generated description is used to refine predictions of the trajectories of multiple agents. We train and validate our model on the Argoverse dataset, and demonstrate improved accuracy results in trajectory prediction. In addition, our model is more interpretable: it presents part of its reasoning in plain language as captions, which can aid model development and can aid in building confidence in the model before deploying it.

}, doi = {10.1109/ICRA46639.2022.9811928}, url = {https://ieeexplore.ieee.org/document/9811928}, author = {Kuo, Yen-Ling and Huang, Xin and Barbu, Andrei and McGill, Stephen G. and Katz, Boris and Leonard, John J. and Rosman, Guy} } @article {5069, title = {Compositional RL Agents That Follow Language Commands in Temporal Logic}, journal = {Frontiers in Robotics and AI}, volume = {8}, year = {2021}, month = {07/2022}, abstract = {

We demonstrate how a reinforcement learning agent can use compositional recurrent neural networks to learn to carry out commands specified in linear temporal logic (LTL). Our approach takes as input an LTL formula, structures a deep network according to the parse of the formula, and determines satisfying actions. This compositional structure of the network enables zero-shot generalization to significantly more complex unseen formulas. We demonstrate this ability in multiple problem domains with both discrete and continuous state-action spaces. In a symbolic domain, the agent finds a sequence of letters that satisfy a specification. In a Minecraft-like environment, the agent finds a sequence of actions that conform to a formula. In the Fetch environment, the robot finds a sequence of arm configurations that move blocks on a table to fulfill the commands. While most prior work can learn to execute one formula reliably, we develop a novel form of multi-task learning for RL agents that allows them to learn from a diverse set of tasks and generalize to a new set of diverse tasks without any additional training. The compositional structures presented here are not specific to LTL, thus opening the path to RL agents that perform zero-shot generalization in other compositional domains.

}, doi = {10.3389/frobt.2021.689550}, url = {https://www.frontiersin.org/articles/10.3389/frobt.2021.689550/full}, author = {Kuo, Yen-Ling and Katz, Boris and Andrei Barbu} } @conference {4804, title = {Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas}, booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2020}, address = {Las Vegas, NV, USA}, abstract = {

We demonstrate a reinforcement learning agent which uses a compositional recurrent neural network that takes as input an LTL formula and determines satisfying actions. The input LTL formulas have never been seen before, yet the network performs zero-shot generalization to satisfy them. This is a novel form of multi-task learning for RL agents where agents learn from one diverse set of tasks and generalize to a new set of diverse tasks. The formulation of the network enables this capacity to generalize. We demonstrate this ability in two domains. In a symbolic domain, the agent finds a sequence of letters that is accepted. In a Minecraft-like environment, the agent finds a sequence of actions that conform to the formula. While prior work could learn to execute one formula reliably given examples of that formula, we demonstrate how to encode all formulas reliably. This could form the basis of new multitask agents that discover sub-tasks and execute them without any additional training, as well as the agents which follow more complex linguistic commands. The structures required for this generalization are specific to LTL formulas, which opens up an interesting theoretical question: what structures are required in neural networks for zero-shot generalization to different logics?

}, doi = {10.1109/IROS45743.2020.9341325}, url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9340668}, author = {Kuo, Yen-Ling and Katz, Boris and Andrei Barbu} }