Deploying robots in real-world environments, such as households and manufacturing lines,
requires generalization across novel task specifications without violating safety constraints …

We study reinforcement learning (RL) for decision processes with non-Markovian reward, in
which high-level knowledge in the form of reward machines is available to the learner …

Compositionality is a critical aspect of scalable system design. Here, we focus on Boolean
composition of learned tasks as opposed to functional or sequential composition. Existing …

Compositional Generalization (CG) embodies the ability to comprehend novel combinations
of familiar concepts, representing a significant cognitive leap in human intellectual …

This paper presents a new approach to design verified compositions of Neural Network (NN)
controllers for autonomous systems with tasks captured by Linear Temporal Logic (LTL) …

Large Language Models (LLMs) have demonstrated remarkable potential in scientific
domains, yet a fundamental question remains unanswered: Can we simulate human …

This work develops a zero-shot mechanism for an agent to satisfy a Linear Temporal Logic
(LTL) specification given existing task primitives. Oftentimes, autonomous robots need to …

Several methods have been proposed recently to learn neural network (NN) controllers for
autonomous agents, with unknown and stochastic dynamics, tasked with complex missions …

Deploying robots in real-world environments, such as households and manufacturing lines,
requires generalization across novel task specifications without violating safety constraints …

Compositionality is a critical aspect of scalable system design. Reinforcement learning (RL)
has recently shown substantial success in task learning, but has only recently begun to truly …