10 papers
Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning
Beyazit Yalcinkaya, Marcell Vazquez-Chanlatte, Ameesh Shah +2
We study learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution. In this setting, using automata to rep…
FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation
Yuanhang Zhang, Yifu Yuan, Prajwal Gurunath +7
Humanoid loco-manipulation holds transformative potential for daily service and industrial tasks, yet achieving precise, robust whole-body control with 3D end-effector force intera…
Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving
Akshar Tumu, Henrik I. Christensen, Marcell Vazquez-Chanlatte +2
Lane-topology prediction is a critical component of safe and reliable autonomous navigation. An accurate understanding of the road environment aids this task. We observe that this…
SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs
Hitvarth Diwanji, Jing-Yan Liao, Akshar Tumu +3
High-definition maps (HD maps) are detailed and informative maps capturing lane centerlines and road elements. Although very useful for autonomous driving, HD maps are costly to bu…
: Learning Automata from Examples using Natural Language Oracles
Marcell Vazquez-Chanlatte, Karim Elmaaroufi, Stefan J. Witwicki +2
Expert demonstrations have proven an easy way to indirectly specify complex tasks. Recent algorithms even support extracting unambiguous formal specifications, e.g. deterministic f…
Provably Correct Automata Embeddings for Optimal Automata-Conditioned Reinforcement Learning
Beyazit Yalcinkaya, Niklas Lauffer, Marcell Vazquez-Chanlatte +1
Automata-conditioned reinforcement learning (RL) has given promising results for learning multi-task policies capable of performing temporally extended objectives given at runtime,…