Lin Guan

According to our database, Lin Guan authored at least 17 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Robust Planning with LLM-Modulo Framework: Case Study in Travel Planning.

Atharva Gundawar, Mudit Verma, Lin Guan, Karthik Valmeekam, Siddhant Bhambri, Subbarao Kambhampati

CoRR, 2024

"Task Success" is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors.

Lin Guan, Yifan Zhou, Denis Liu, Yantian Zha, Heni Ben Amor, Subbarao Kambhampati

CoRR, 2024

LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks.

Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Kaya Stechly, Mudit Verma, Siddhant Bhambri, Lucas Saldyt, Anil Murthy

CoRR, 2024

Position: LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks.

Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Saldyt, Anil Murthy

Proceedings of the Forty-first International Conference on Machine Learning, 2024

On the Pitfalls of Learning to Cooperate with Self Play Agents Checkpointed to Capture Humans of Diverse Skill Levels.

Upasana Biswas, Lin Guan, Subbarao Kambhampati

Proceedings of the Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024

Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning.

Yantian Zha, Lin Guan, Subbarao Kambhampati

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning.

Lin Guan, Karthik Valmeekam, Sarath Sreedharan, Subbarao Kambhampati

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences.

Lin Guan, Karthik Valmeekam, Subbarao Kambhampati

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion.

Utkarsh Soni, Sarath Sreedharan, Mudit Verma, Lin Guan, Matthew Marquez, Subbarao Kambhampati

CoRR, 2022

Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity.

Lin Guan, Sarath Sreedharan, Subbarao Kambhampati

Proceedings of the International Conference on Machine Learning, 2022

Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems.

Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma, Yantian Zha, Lin Guan

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation.

Lin Guan, Mudit Verma, Sihang Guo, Ruohan Zhang, Subbarao Kambhampati

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping.

Yantian Zha, Siddhant Bhambri, Lin Guan

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

2020

Explanation Augmented Feedback in Human-in-the-Loop Reinforcement Learning.

Lin Guan, Mudit Verma, Subbarao Kambhampati

CoRR, 2020

Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset.

Ruohan Zhang, Calen Walshe, Zhuode Liu, Lin Guan, Karl S. Muller, Jake Alden Whritner, Luxin Zhang, Mary M. Hayhoe, Dana H. Ballard

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset.

Ruohan Zhang, Zhuode Liu, Lin Guan, Luxin Zhang, Mary M. Hayhoe, Dana H. Ballard

CoRR, 2019

Leveraging Human Guidance for Deep Reinforcement Learning Tasks.

Ruohan Zhang, Faraz Torabi, Lin Guan, Dana H. Ballard, Peter Stone

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019