Publications
2023
A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches
M. Maliha, G. Habibi, M. Atiquzzaman, "A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches,"; acsis2023. [Online]. Available: https://annals-csis.org/proceedings/2023/pliks/9832.pdf. [Accessed: 1-oct-2023].
2022
Visual navigation for autonomous vehicles: An open-source hands-on robotics course at MIT
L. Carlone, K. Khosoussi, V. Tzoumas, G. Habibi, M. Ryll, R. Talak, J. Shi, and P. Antonante, "Visual navigation for autonomous vehicles: An open-source hands-on robotics course at MIT,"; arXiv.org, 01-Jun-2022. [Online]. Available: https://arxiv.org/abs/2206.00777. [Accessed: 30-Jan-2023].
Motivating physical activity via competitive human-robot interaction
B. Yang, G. Habibi, P. Lancaster, B. Boots, and J. Smith, "Motivating physical activity via competitive human-robot interaction," PMLR, 11-Jan-2022. [Online]. Available: https://proceedings.mlr.press/v164/yang22e.html. [Accessed: 30-Jan-2023].
2021
Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments
S. Raja, G. Habibi and J. P. How, “Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments,” IEEE Access, vol. 10, pp. 19753-19767, 2022, doi: 10.1109/ACCESS.2021.3138857.
Reachability analysis of neural feedback loops: Semantic scholar
M. Everett, G. Habibi, C. Sun, and J. How, “[pdf] reachability analysis of neural feedback loops: Semantic scholar,” IEEE Access, 01-Jan-1970. [Online]. Available: https://www.semanticscholar.org/paper/Reachability-Analysis-of-Neural-Feedback-Loops-Everett-Habibi/b4b07b332e948fde15b77d4fa696116e1d5e1370. [Accessed: 30-Jan-2023].
A policy gradient algorithm for learning to learn in multiagent reinforcement learning
D. K. Kim, M. Liu, M. D. Riemer, C. Sun, M. Abdulhai, G. Habibi, S. Lopez-Cot, G. Tesauro, and J. How, “A policy gradient algorithm for learning to learn in multiagent reinforcement learning,” PMLR, 01-Jul-2021. [Online]. Available: https://proceedings.mlr.press/v139/kim21g.html. [Accessed: 30-Jan-2023].
Efficient reachability analysis of closed-loop systems with neural network controllers: Semantic scholar
M. Everett, G. Habibi, and J. How, “[PDF] efficient reachability analysis of closed-loop systems with neural network controllers: Semantic scholar,” 2021 IEEE International Conference on Robotics and Automation (ICRA), 01-Jan-1970. [Online]. Available: https://www.semanticscholar.org/paper/Efficient-Reachability-Analysis-of-Closed-Loop-with-Everett-Habibi/0952cbae6f191f0f55000a2ffb20b35aba1a54dd. [Accessed: 30-Jan-2023].
Competitive physical human-robot game play: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
B. Y. U. of Washington, B. Yang, U. of Washington, X. X. U. of Washington, X. Xie, Golnaz Habibi Massachusetts Institute of Technology, G. Habibi, M. I. of Technology, Joshua R. Smith University of Washington, J. R. Smith, M. S. University, Inesc-Id, U. of Auckland, U. of N. Reno, U. of C. Boulder, and O. M. V. A. Metrics, “Competitive physical human-robot game play: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction,” ACM Conferences, 01-Mar-2021. [Online]. Available: https://dl.acm.org/doi/abs/10.1145/3434074.3447168. [Accessed: 30-Jan-2023].
Human trajectory prediction using similarity-based multi-model fusion: Semantic scholar
G. Habibi and J. How, “Human trajectory prediction using similarity-based multi-model fusion: Semantic scholar,” IEEE Robotics and Automation Letters, 01-Apr-2021. [Online]. Available: https://www.semanticscholar.org/paper/Human-Trajectory-Prediction-Using-Similarity-Based-Habibi-How/d21a09c71a0efef745b5c5c00876d6e875078f46. [Accessed: 30-Jan-2023].
2020
Robustness analysis of neural networks via efficient partitioning with applications in control systems
M. Everett, G. Habibi, and J. P. How, “Robustness analysis of neural networks via efficient partitioning with applications in control systems,” arXiv.org, 07-Dec-2020. [Online]. Available: https://arxiv.org/abs/2010.00540. [Accessed: 30-Jan-2023].
Robustness analysis of neural networks via efficient partitioning
M. Everett, G. Habibi, and J. How, “(PDF) robustness analysis of neural networks via efficient partitioning ...,” ResearchGate.net. [Online]. Available: https://www.researchgate.net/publication/344447310_Robustness_Analysis_of_Neural_Networks_via_Efficient_Partitioning_Theory_and_Applications_in_Control_Systems. [Accessed: 31-Jan-2023].
An incremental learning approach for pedestrian trajectory prediction
G. Habibi, N. Jaipuria, and J. P. How, “Sila: An incremental learning approach for pedestrian trajectory prediction,” CVF Open Access, 01-Jan-1970. [Online]. Available: https://openaccess.thecvf.com/content_CVPRW_2020/html/w66/Habibi_SILA_An_Incremental_Learning_Approach_for_Pedestrian_Trajectory_Prediction_CVPRW_2020_paper.html. [Accessed: 30-Jan-2023].
Heterogeneous knowledge transfer via hierarchical teaching in
D.-K. Kim, J. P. How, G. Habibi, S. Mourad, M. Campbell, G. Tesauro, M. Riemer, S. Lopez-Cot, S. Omidshafiei, and M. Liu, “Heterogeneous knowledge transfer via hierarchical teaching in ...” [Online]. Available: http://aaai-rlg.mlanctot.info/papers/AAAI19-RLG-Paper44.pdf. [Accessed: 31-Jan-2023].
2019
Incremental learning of motion primitives for pedestrian trajectory prediction at intersections
G. Habibi, N. Japuria, and J. P. How, “Incremental learning of motion primitives for pedestrian trajectory prediction at intersections,” arXiv.org, 21-Nov-2019. [Online]. Available: https://arxiv.org/abs/1911.09476. [Accessed: 15-Feb-2023].
Learning hierarchical teaching policies for cooperative agents
D.-K. Kim, M. Liu, S. Omidshafiei, S. Lopez-Cot, M. Riemer, G. Habibi, G. Tesauro, S. Mourad, M. Campbell, and J. P. How, “Learning hierarchical teaching policies for cooperative agents,” arXiv.org, 18-May-2020. [Online]. Available: https://arxiv.org/abs/1903.03216. [Accessed: 15-Feb-2023].
Learning hierarchical teaching in cooperative multiagent reinforcement learning
Learning hierarchical teaching in cooperative multiagent reinforcement learning.
Heterogeneous Knowledge Transfer via Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning
Heterogeneous Knowledge Transfer via Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning.
2018
Learning in the curbside coordinate frame for a transferable pedestrian trajectory prediction model: Semantic scholar
N. Jaipuria, G. Habibi, and J. How, “Learning in the curbside coordinate frame for a transferable pedestrian trajectory prediction model: Semantic scholar,” 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 01-Jan-1970. [Online]. Available: https: //www.semanticscholar.org/paper/Learning-in-the-Curbside-Coordinate-Frame-for-a-Jaipuria-Habibi/a5bf12b89fb56ee1454bd7b887e0afed4e06116e. [Accessed: 15-Feb-2023].
Continual Learning of Pedestrian Motion Behaviors using Motion Primitives
Continual Learning of Pedestrian Motion Behaviors using Motion Primitives.
Transferable pedestrian motion prediction models at intersections: Semantic scholar
M. Shen, G. Habibi, and J. How, “[PDF] transferable pedestrian motion prediction models at intersections: Semantic scholar,” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-Jan-1970. [Online]. Available: https://www.semanticscholar.org/paper/Transferable-Pedestrian-Motion-Prediction-Models-at-Shen-Habibi/9988c89efe8225f8c9b4a143d314aa7205ec6b0a. [Accessed: 15-Feb-2023].
Context-aware pedestrian motion prediction in urban intersections
G. Habibi, N. Jaipuria, and J. P. How, “Context-aware pedestrian motion prediction in urban intersections,” arXiv.org, 25-Jun-2018. [Online]. Available: https://arxiv.org/abs/1806.09453. [Accessed: 15-Feb-2023].
2017
CASNSC: A context-based approach for accurate pedestrian motion
N. Japuria, G. Habibi, and J. P. How, “CASNSC: A context-based approach for accurate pedestrian motion...,” OpenReview, 31-Oct-2017. [Online]. Available: https://openreview.net/forum?id=rJ26HSLRb. [Accessed: 15-Feb-2023].
Stable laser interest point selection for place recognition in a forest
M. Giamou, Y. Babich, G. Habibi, and J. P. How, “Stable laser interest point selection for place recognition in a forest ...” [Online]. Available: https://ieeexplore.ieee.org/document/8206292/. [Accessed: 15-Feb-2023].