| Reinforcement learning with temporal logic rewards X Li, CI Vasile, C Belta 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 180 | 2017 |
| A formal methods approach to interpretable reinforcement learning for robotic planning X Li, Z Serlin, G Yang, C Belta Science Robotics 4 (37), 2019 | 85 | 2019 |
| A policy search method for temporal logic specified reinforcement learning tasks X Li, Y Ma, C Belta 2018 Annual American Control Conference (ACC), 240-245, 2018 | 72 | 2018 |
| Temporal logic guided safe reinforcement learning using control barrier functions X Li, C Belta arXiv preprint arXiv:1903.09885, 2019 | 38 | 2019 |
| Reactive Sampling-Based Path Planning with Temporal Logic Specifications CI Vasile, X Li, C Belta International Journal of Robotics Research, 2020 | 32 | 2020 |
| Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features X Li, G Rosman, I Gilitschenski, CI Vasile, J DeCastro, S Karaman, D Rus IEEE Robotics and Automation Letters, 3459-3466, 2021 | 30 | 2021 |
| Barriernet: Differentiable control barrier functions for learning of safe robot control W Xiao, TH Wang, R Hasani, M Chahine, A Amini, X Li, D Rus IEEE Transactions on Robotics, 2023 | 17 | 2023 |
| Barriernet: A safety-guaranteed layer for neural networks W Xiao, R Hasani, X Li, D Rus arXiv preprint arXiv:2111.11277, 2021 | 17 | 2021 |
| The Logical Options Framework B Araki, X Li, K Vodrahalli, J DeCastro, MJ Fry, D Rus International Conference on Machine Learning, 307-317, 2021 | 17 | 2021 |
| Automata guided reinforcement learning with demonstrations X Li, Y Ma, C Belta arXiv preprint arXiv:1809.06305, 2018 | 14 | 2018 |
| Differentiable Logic Layer for Rule Guided Trajectory Prediction X Li, G Rosman, I Gilitschenski, J DeCastro, CI Vasile, S Karaman, D Rus Conference on Robot Learning, 2020 | 11 | 2020 |
| Automata Guided Semi-Decentralized Multi-Agent Reinforcement Learning CC Sun, X Li, C Belta American Control Conference (ACC), 2020 | 10 | 2020 |
| Task frame estimation during model-based teleoperation for satellite servicing X Li, P Kazanzides 2016 IEEE International Conference on Robotics and Automation (ICRA), 2834-2839, 2016 | 8 | 2016 |
| A formal methods approach to interpretability, safety and composability for reinforcement learning X Li Boston University, 2020 | 6 | 2020 |
| A hierarchical reinforcement learning method for persistent time-sensitive tasks X Li, C Belta arXiv preprint arXiv:1606.06355, 2016 | 5 | 2016 |
| Learning An Explainable Trajectory Generator Using The Automaton Generative Network (AGN) X Li, G Rosman, I Gilitschenski, CI Araki, Brandon, Vasile, S Karaman, ... The IEEE Robotics and Automation Letters, 2022 | 4 | 2022 |
| Learning A Risk-Aware Trajectory Planner FromDemonstrations Using Logic Monitor X Li, J DeCastro, CI Vasile, S Karaman, D Rus Conference on Robot Learning, 2021 | 4* | 2021 |
| Parameter estimation and anomaly detection while cutting insulation during telerobotic satellite servicing X Li, P Kazanzides 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015 | 4 | 2015 |
| Automata-guided hierarchical reinforcement learning for skill composition X Li, Y Ma, C Belta arXiv preprint arXiv:1711.00129, 2017 | 3 | 2017 |
| Lie-theoretic multi-robot localization X Li, GS Chirikjian Riemannian Computing in Computer Vision, 165-186, 2016 | 3 | 2016 |
Calin BeltaProfessor, Boston UniversityBestätigte E-Mail-Adresse bei bu.edu
