![[2602.18716] Temporal Action Representation Learning for Tactical Resource Control and Subsequent Maneuver Generation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.18716] Temporal Action Representation Learning for Tactical Resource Control and Subsequent Maneuver Generation
Summary
This paper presents TART, a Temporal Action Representation learning framework designed for tactical resource control and maneuver generation in autonomous robotic systems, addressing the challenges of limited resources and complex dynamics.
Why It Matters
As autonomous systems increasingly operate in dynamic environments with constraints on resources, understanding how to effectively manage these resources while ensuring coherent maneuvers is crucial. This research advances the field by introducing a novel framework that captures the temporal dependencies between resource usage and tactical maneuvers, potentially improving operational efficiency in critical applications like military simulations and robotics.
Key Takeaways
- TART framework effectively integrates resource control with maneuver generation.
- Utilizes contrastive learning to capture temporal dependencies in tactical decisions.
- Demonstrates superior performance in resource-constrained environments compared to existing methods.
- Applicable in both maze navigation and air combat scenarios.
- Highlights the importance of multi-modal decision-making in autonomous systems.
Computer Science > Robotics arXiv:2602.18716 (cs) [Submitted on 21 Feb 2026] Title:Temporal Action Representation Learning for Tactical Resource Control and Subsequent Maneuver Generation Authors:Hoseong Jung, Sungil Son, Daesol Cho, Jonghae Park, Changhyun Choi, H. Jin Kim View a PDF of the paper titled Temporal Action Representation Learning for Tactical Resource Control and Subsequent Maneuver Generation, by Hoseong Jung and 5 other authors View PDF HTML (experimental) Abstract:Autonomous robotic systems should reason about resource control and its impact on subsequent maneuvers, especially when operating with limited energy budgets or restricted sensing. Learning-based control is effective in handling complex dynamics and represents the problem as a hybrid action space unifying discrete resource usage and continuous maneuvers. However, prior works on hybrid action space have not sufficiently captured the causal dependencies between resource usage and maneuvers. They have also overlooked the multi-modal nature of tactical decisions, both of which are critical in fast-evolving scenarios. In this paper, we propose TART, a Temporal Action Representation learning framework for Tactical resource control and subsequent maneuver generation. TART leverages contrastive learning based on a mutual information objective, designed to capture inherent temporal dependencies in resource-maneuver interactions. These learned representations are quantized into discrete codebook entries th...