25 citations · 115 across the 59 of their papers we have counts for
17 papers · 1 filter
Video2STL: Grounding VLM-Generated Temporal Specifications for Robot Learning
Merve Atasever, Keyan Azbijari, Cagan Bakirci +6
Video-based policy learning is particularly promising, as it illustrates target behaviors without requiring action annotations or embodiment-matched demonstrations. A central chall…
From LLM-Generated Specifications to Learned Quadruped Locomotion
Merve Atasever, Keyan Azbijari, Cagan Bakirci +5
Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. Designing reward functions requir…
Safety-aware Model Predictive Path Integral Control with Signal Temporal Logic
Yiqi Zhao, Taekyung Kim, Hideki Okamoto +4
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-awar…
Logic-VLA: A Temporal Logic Conditioned Vision-Language-Action Model
Celina Shiyu Wang, Yiqi Zhao, Junjie Ye +2
Vision-language-action (VLA) models can follow natural-language (NL) task instructions, but such instructions may not precisely specify safety-critical or spatiotemporal requiremen…
Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications
Merve Atasever, Cagan Bakirci, Alfredo Reina Corona +3
Reinforcement learning (RL) for quadruped locomotion commonly depends on fixed, hand-crafted, and Markovian reward functions that may limit interpretability of learned policies and…
Duet: Dual-Robot Understanding via Efficient Teaching
Yiqi Zhao, Ruohai Ge, Celina Shiyu Wang +10
Dual-robot collaboration enables tasks that exceed the reach and payload of a single robot, such as collaboratively transporting objects across environments and executing coordinat…
Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic
Lorenzo Bonin, Francesco Giacomarra, Luca Bortolussi +2
The rapid advancement of autonomous driving (AD) technologies has outpaced the development of robust safety evaluation methods. Conventional testing relies on exposing AD systems t…
Benchmarking Autonomous Driving Planners Across Leaderboards: A Unified CARLA-Based Evaluation
Merve Atasever, Alfredo Reina Corona, Zhuochen Liu +5
Autonomous driving remains a highly active research domain that seeks to enable vehicles to perceive dynamic environments, predict the future trajectories of traffic agents such as…
PCA-DDReach: Efficient Statistical Reachability Analysis of Stochastic Dynamical Systems via Principal Component Analysis
Navid Hashemi, Lars Lindemann, Jyotirmoy Deshmukh
This study presents a scalable data-driven algorithm designed to efficiently address the challenging problem of reachability analysis. Analysis of cyber-physical systems (CPS) reli…
Coordinating Spinal and Limb Dynamics for Enhanced Sprawling Robot Mobility
Merve Atasever, Ali Okhovat, Azhang Nazaripouya +4
Sprawling locomotion in vertebrates, particularly salamanders, demonstrates how body undulation and spinal mobility enhance stability, maneuverability, and adaptability across comp…
Statistical Reachability Analysis of Stochastic Cyber-Physical Systems under Distribution Shift
Navid Hashemi, Lars Lindemann, Jyotirmoy V. Deshmukh
Reachability analysis is a popular method to give safety guarantees for stochastic cyber-physical systems (SCPSs) that takes in a symbolic description of the system dynamics and us…
Signal Temporal Logic-Guided Apprenticeship Learning
Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis
Apprenticeship learning crucially depends on effectively learning rewards, and hence control policies from user demonstrations. Of particular difficulty is the setting where the de…
Formalizing and Evaluating Requirements of Perception Systems for Automated Vehicles using Spatio-Temporal Perception Logic
Mohammad Hekmatnejad, Bardh Hoxha, Jyotirmoy V. Deshmukh +2
Automated vehicles (AV) heavily depend on robust perception systems. Current methods for evaluating vision systems focus mainly on frame-by-frame performance. Such evaluation metho…
Learning Performance Graphs from Demonstrations via Task-Based Evaluations
Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis
In the learning from demonstration (LfD) paradigm, understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots. Without…
PerceMon: Online Monitoring for Perception Systems
Anand Balakrishnan, Jyotirmoy Deshmukh, Bardh Hoxha +2
Perception algorithms in autonomous vehicles are vital for the vehicle to understand the semantics of its surroundings, including detection and tracking of objects in the environme…
Learning from Demonstrations using Signal Temporal Logic
Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis
Learning-from-demonstrations is an emerging paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design r…
Model-based Reinforcement Learning from Signal Temporal Logic Specifications
Parv Kapoor, Anand Balakrishnan, Jyotirmoy V. Deshmukh
Techniques based on Reinforcement Learning (RL) are increasingly being used to design control policies for robotic systems. RL fundamentally relies on state-based reward functions…