Joe Eappen
PhD Candidate, ECE @ Purdue
News
- NEW (May 2025) Prelim done, on to the final stretch!
- (February 2025) Paper in Learning Scoring Rules in Autonomous Driving Planning Systems accepted to RA-L! Congrats Zikang!
- (September 2024) Paper in Scaling Specification-Guided Multi-Agent Control accepted to CoRL 2024!
- (May 2024) Paper in Offline Reinforcement Learning worked on during my summer at JPMorgan Chase & Co. accepted to ICML 2024! It was a pleasure collaborating with Sujay and Alec.
- (January 2024) Paper in Differentiable Signal Temporal Logic (STL) applied to robotics accepted to ICRA 2024, congrats Zikang!
- (October 2023) Paper in MCMC thinning using Stein Discrepancy worked on during my summer at JPMorgan Chase & Co. accepted to SIAM SIMODS!
- (June 2022) 2 Papers accepted to ECML PKDD 2022 in Temporal Logic controllers for Multi-agent RL systems and Adversarial Attacks to RL controllers. 1 accepted to IROS 2022 in Safer RL for Navigation!
Research
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FLoRA: A Framework for Learning Scoring Rules in Autonomous Driving Planning Systems
Zikang Xiong, Joe Eappen, and Suresh Jagannathan.
IEEE Robotics and Automation Letters (IEEE RA-L)
[paper] [code] [arXiv] [website] [bibTex]
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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] [arXiv] [website] [bibTex]
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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]
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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]
Patents
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System and method for providing information-directed pessimism for offline reinforcement learning
Alec Koppel, Sujay Bhatt, Joe Eappen, and Sumitra Ganesh
US Patent Application No. 18/379,406, filed October 2023, published as US20250124334A1 [link]