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Jyotirmoy V. Deshmukh

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Preprint Sep 2026

Agent-Based Evolutionary Dynamics for Mixed Autonomy Weaving Ramps

Existing models of mixed-autonomy weaving ramps characterize how altruistic connected and automated vehicles (CAVs) can improve traffic efficiency at the population level, but provide limited insight into how such behavior emerges from decentralized vehicle interactions or how it is affected by finite populations, hete...

Sheryl Paul, Ke-Xin Wang, Ruo-Lin Li et al. · 0 citations
Preprint Aug 2026

Safety-aware Model Predictive Path Integral Control with Signal Temporal Logic

Safety-aware motion planning remains a challenge in robotics, especially when missions are time-critical and are under complex specifications. In this paper, we propose safety-aware-stl-mppi, a computationally efficient sampling-based receding-horizon planning framework designed to promote satisfaction of constraints e...

Yi-Qi Zhao, Taekyung Kim, Hideki Okamoto et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Video2STL: Grounding VLM-Generated Temporal Specifications for Robot Learning

Video-based policy learning is particularly promising, as it illustrates target behaviors without requiring action annotations or embodiment-matched demonstrations. A central challenge is deciding what information should be transferred from the video to the robot. Existing approaches commonly convert visual observation...

Merve Atasever, Keyan Azbijari, Cagan Bakirci et al. · 0 citations
Jul 2026

Multi-Agent Planning with Spatio-Temporal and Topological Constraints using STL-GO

Multi-agent planning problems arise in a variety of engineering applications, such as multi-robot wildfire fighting and unmanned aerial inspection in factories. A particular challenge is the existence of spatio-temporal (i.e., when and/or where an agent should do what) and topological constraints (i.e., how agents shou...

Sheryl Paul, Vidisha Kudalkar, Anand Balakrishnan et al. · 0 citations
#artificial intelligence Preprint Sep 2026

From LLM-Generated Specifications to Learned Quadruped Locomotion

Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. Designing reward functions requires substantial manual engineering, and it is often unclear which local rewards will induce the desired global behavior. Shaped rewards from...

Merve Atasever, Keyan Azbijari, Cagan Bakirci et al. · 0 citations
Preprint Aug 2026

Logic-VLA: A Temporal Logic Conditioned Vision-Language-Action Model

Logic-VLA is introduced, a formal-requirement-aware VLA that conditions on Signal Temporal Logic (STL) specifications supplied at inference time, showing that a single VLA can adapt its behavior to varying formal requirements without requiring a separate policy for each specification.

Celina Shiyu Wang, Yiqi Zhao, Junjie Ye et al. · 1 citation

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