Scaling Safe Multi-Agent Control for Signal Temporal Logic Specifications
Joe Eappen, Zikang Xiong, Dipam Patel, Aniket Bera and Suresh Jagannathan.
Annual Conference on Robot Learning (CoRL 2024) , Safe-ROL Workshop @ CoRL 2024
[paper] [code] [website] [bibTex]
Information-Directed Pessimism for Offline Reinforcement Learning
Alec Koppel, Sujay Bhatt, Jiacheng Guo, Joe Eappen, Mengdi Wang, and Sumitra Ganesh.
International Conference on Machine Learning (ICML 2024)
[paper] [code] [bibTex]
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications
Zikang Xiong, Daniel Lawson, Joe Eappen, Ahmed H. Qureshi, and Suresh Jagannathan.
IEEE International Conference on Robotics and Automation (ICRA 2024)
[paper] [demos] [arXiv] [bibTex]
Online MCMC Thinning with Kernelized Stein Discrepancy
Alec Koppel*, Joe Eappen*, Sujay Bhatt*, Cole Hawkins*, and Sumitra Ganesh.
SIAM Journal on Mathematics of Data Science (SIMODS)
[paper] [arXiv] [bibTex]
*- Equal Contribution
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems
Joe Eappen and Suresh Jagannathan.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022) , ALA Workshop @ AAMAS 2022
[paper] [code] [arXiv] [bibTex]
Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising
Zikang Xiong, Joe Eappen, He Zhu, and Suresh Jagannathan.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022)
[paper] [arXiv] [bibTex]
Model-free Neural Lyapunov Control for Safe Robot Navigation
Zikang Xiong, Joe Eappen, Ahmed H. Qureshi, and Suresh Jagannathan.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
[paper] [demos] [arXiv] [bibTex]
Robustness to Adversarial Attacks in Learning-Enabled Controllers
Zikang Xiong, Joe Eappen, He Zhu, and Suresh Jagannathan.
ALA Workshop @ AAMAS 2021
[paper] [code] [talk] [arXiv] [bibTex]