| Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning L Guan*, K Valmeekam*, S Sreedharan, S Kambhampati NeurIPS 2023, 2023 | 128 | 2023 |
| Leveraging human guidance for deep reinforcement learning tasks R Zhang, F Torabi, L Guan, DH Ballard, P Stone IJCAI 2019, 2019 | 112 | 2019 |
| LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks S Kambhampati, K Valmeekam, L Guan, K Stechly, M Verma, S Bhambri, ... ICML 2024 (Position Paper), 2024 | 90* | 2024 |
| Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset R Zhang, C Walshe, Z Liu, L Guan, KS Muller, JA Whritner, L Zhang, ... AAAI 2020, 2019 | 80 | 2019 |
| Widening the pipeline in human-guided reinforcement learning with explanation and context-aware data augmentation L Guan, M Verma, SS Guo, R Zhang, S Kambhampati NeurIPS 2021 (Spotlight), 2021 | 62* | 2021 |
| Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems S Kambhampati, S Sreedharan, M Verma, Y Zha, L Guan AAAI 2022 (Blue Sky Track), 2021 | 52 | 2021 |
| Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity L Guan*, S Sreedharan*, S Kambhampati ICML 2022, 2022 | 26 | 2022 |
| Robust Planning with LLM-Modulo Framework: Case Study in Travel Planning A Gundawar, M Verma, L Guan, K Valmeekam, S Bhambri, ... arXiv preprint arXiv:2405.20625, 2024 | 13 | 2024 |
| "Task Success" is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors L Guan, Y Zhou, D Liu, Y Zha, HB Amor, S Kambhampati COLM 2024, 2024 | 10 | 2024 |
| Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping Y Zha, S Bhambri, L Guan IROS 2021, 2021 | 9 | 2021 |
| Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences L Guan, K Valmeekam, S Kambhampati ICLR 2023, 2023 | 8 | 2023 |
| Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion U Soni, S Sreedharan, M Verma, L Guan, M Marquez, S Kambhampati NeurIPS 2022 Workshop on Human-in-the-Loop Learning, 2022 | 6 | 2022 |
| Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning Y Zha, L Guan, S Kambhampati AAAI 2024, 2021 | 5 | 2021 |
| On the role of large language models in planning, July 2023. Tutorial presented at the International Conference on Automated Planning and Scheduling (ICAPS), Prague S Kambhampati, K Valmeekam, M Marquez, L Guan | 4 | 2023 |
| Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning S Bhambri, A Bhattacharjee, D Kalwar, L Guan, H Liu, S Kambhampati arXiv preprint arXiv:2405.15194, 2024 | 1 | 2024 |
| On the Pitfalls of Learning to Cooperate with Self Play Agents Checkpointed to Capture Humans of Diverse Skill Levels U Biswas, L Guan, S Kambhampati HRI 2024 LBR, 2024 | | 2024 |
| Taming the Sample Complexity in Agentifying AI Systems by the Exploitation of Explicit Human Knowledge L Guan Arizona State University, 2024 | | 2024 |
| Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers L Guan, X Xiao, M Chen, Y Cheng AAAI-22 Workshop on Practical Deep Learning in the Wild, 2021 | | 2021 |
| Robust Scheduling with the LLM-Modulo Framework: A Case Study in TravelPlanner A Gundawar, M Verma, K Stechly, K Valmeekam, L Guan, S Bhambri, ... | | |
| LLM-Modulo Frameworks as Compound AI Architectures for Robust Planning S Kambhampati, K Valmeekam, L Guan, M Verma, S Bhambri, K Stechly, ... | | |
