arXiv:2607. 10918v1 Announce Type: new Abstract: Learning temporal logic specifications from system demonstrations is essential for tasks such as formal verification and controller synthesis, especially in safety-critical domains.
arXiv:2609.01032v1 Announce Type: cross Abstract: Automated mining of formal specifications is vital for verifying real-time systems. However, existing passive learning approaches remain restricted t...
arXiv:2608. 13678v1 Announce Type: cross Abstract: A central goal of robot learning is to enable robots to execute rich instructions specified at runtime.
arXiv:2607. 08899v1 Announce Type: cross Abstract: Signal Temporal Logic (STL) is increasingly used to describe interpretable objectives and constraints for optimal control and learning methods, especially when no target time series data is available.
arXiv:2608. 13625v1 Announce Type: new Abstract: Signal temporal logic (STL) provides a formal language for specifying real-time properties of real-valued observations, along with a quantitative robustness score for monitoring satisfaction.
arXiv:2602. 06746v2 Announce Type: replace Abstract: We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy capable of generalising to arbitrary, possibly unseen tasks.
arXiv:2602. 14344v2 Announce Type: replace-cross Abstract: We study instruction following in multi-task reinforcement learning, where an agent must zero-shot execute novel tasks not seen during training.
arXiv:2606. 18519v1 Announce Type: cross Abstract: Though robotic systems are now being commercialized and deployed in various industries, many of these systems are highly specialized and often require an advanced skill set to operate and ensure they perform as instructed.
arXiv:2606. 08312v1 Announce Type: new Abstract: In this work we study offline reinforcement learning (RL) under temporally extended task constraints expressed in Linear Temporal Logic over finite traces (LTLf).
arXiv:2609.37519v1 Announce Type: cross Abstract: Video-based policy learning is particularly promising, as it illustrates target behaviors without requiring action annotations or embodiment-matched...
arXiv:2607. 15003v1 Announce Type: new Abstract: The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.
The paper introduces an end‑to‑end model‑based reinforcement learning algorithm that synthesises policies satisfying Linear Temporal Logic (LTL) specifications in unknown environments. It synchronises a Limit‑Deterministic Büchi Automaton (LDBA) with a Bayes‑Adaptive Markov Decision Process (BAMDP) and proposes a novel Bayes‑Adaptive Monte‑Carlo Planning (BAMCP) method for approximate Bayes‑optimal strategy synthesis. Experiments on finite and infinite‑horizon tasks show improved property satisfaction and sample efficiency compared to model‑free baselines, and ablation studies confirm the advantage of the new BAMCP over classical variants, including reduced task violations in cautious RL settings.
arXiv:2608. 15509v1 Announce Type: cross Abstract: Task guided agents demonstrate strong performance in a wide range of complex tasks.