HealthSense: Software-defined Mobile-based Clinical Trials. - Aidan Curtis; Amruta Pai; Jian Cao; Nidal Moukaddam; Ashutosh Sabharwal (MobiCom, 2019)

Flexible and Efficient Long-Range Planning Through Curious Exploration. - Aidan Curtis; Minjian Xin; Dilip Arumugam; Kevin T. Feigelis; Daniel Yamins (ICML, 2020)

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. - Chuang Gan 001; Jeremy Schwartz; Seth Alter; Martin Schrimpf; James Traer; Julian De Freitas; Jonas Kubilius; Abhishek Bhandwaldar; Nick Haber; Megumi Sano; Kuno Kim; Elias Wang; et al. (CoRR, 2020)

Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks. - Tom Silver; Rohan Chitnis; Aidan Curtis; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (AAAI, 2021)

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. - Chuang Gan 001; Jeremy Schwartz; Seth Alter; Damian Mrowca; Martin Schrimpf; James Traer; Julian De Freitas; Jonas Kubilius; Abhishek Bhandwaldar; Nick Haber; Megumi Sano; Kuno Kim; et al. (NeurIPS Datasets and Benchmarks, 2021)

Discovering State and Action Abstractions for Generalized Task and Motion Planning. - Aidan Curtis; Tom Silver; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (AAAI, 2022)

Efficient exploration of spatial environments through Map Induction using adaptable compositional map representations. - Sugandha Sharma; Aidan Curtis; Marta Kryven; Josh Tenenbaum 001; Ila Fiete (CogSci, 2022)

Map Induction: Compositional spatial submap learning for efficient exploration in novel environments. - Sugandha Sharma; Aidan Curtis; Marta Kryven; Joshua B. Tenenbaum; Ila R. Fiete (ICLR, 2022)

Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances. - Aidan Curtis; Xiaolin Fang 002; Leslie Pack Kaelbling; Tomás Lozano-Pérez; Caelan Reed Garrett (ICRA, 2022)

PG3: Policy-Guided Planning for Generalized Policy Generation. - Ryan Yang; Tom Silver; Aidan Curtis; Tomás Lozano-Pérez; Leslie Pack Kaelbling (IJCAI, 2022)

Let's Handle It: Generalizable Manipulation of Articulated Objects. - Zhutian Yang; Aidan Curtis (CoRR, 2022)

Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects. - Aidan Curtis; Leslie Pack Kaelbling; Siddarth Jain (ICRA, 2023)

Visibility-Aware Navigation Among Movable Obstacles. - Jose Muguira-Iturralde; Aidan Curtis; Yilun Du; Leslie Pack Kaelbling; Tomás Lozano-Pérez (ICRA, 2023)

Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes. - Nishad Gothoskar; Matin Ghavami; Eric Li; Aidan Curtis; Michael Noseworthy; Karen Chung; Brian Patton; William T. Freeman; Joshua B. Tenenbaum; Mirko Klukas; Vikash K. Mansinghka (CoRR, 2023)

Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction. - Aidan Curtis; Nishanth Kumar; Jing Cao; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRL, 2024)

Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness. - Aidan Curtis; George Matheos; Nishad Gothoskar; Vikash Mansinghka 001; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (Robotics: Science and Systems, 2024)

Towards Practical Finite Sample Bounds for Motion Planning in TAMP. - Seiji Shaw; Aidan Curtis; Leslie Pack Kaelbling; Tomás Lozano-Pérez; Nicholas Roy (CoRR, 2024)

Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly. - Jiankai Sun; Aidan Curtis; Yang You 004; Yan Xu 001; Michael Koehle; Leonidas J. Guibas; Sachin Chitta; Mac Schwager; Hui Li (CoRR, 2024)

Flow-based Domain Randomization for Learning and Sequencing Robotic Skills. - Aidan Curtis; Eric Li; Michael Noseworthy; Nishad Gothoskar; Sachin Chitta; Hui Li; Leslie Pack Kaelbling; Nicole Carey (CoRR, 2025)

Seeing is Believing: Belief-Space Planning with Foundation Models as Uncertainty Estimators. - Linfeng Zhao; Willie McClinton; Aidan Curtis; Nishanth Kumar; Tom Silver; Leslie Pack Kaelbling; Lawson L. S. Wong (CoRR, 2025)

Cognitive maps are generative programs. - Marta Kryven; Cole Wyeth; Aidan Curtis; Kevin Ellis (CoRR, 2025)

LLM-Guided Probabilistic Program Induction for POMDP Model Estimation. - Aidan Curtis; Hao Tang 008; Thiago Veloso; Kevin Ellis; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRR, 2025)