Published in ICAPS, 2026
Also presented at ICAPS Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (Oral) ; CoRL Workshop on Safe and Robust Learning for Operation in the Real World
Dynamic CBS risk budgeting lets visual multi-agent teams push through higher-risk corridors when it pays off, delivering faster, collision-free plans while still honoring the user’s $\Delta$ constraint.
Published in CoRL, 2025
Also presented at RSS Workshop on Scalable and Resilient Multi-Robot Systems
Diffusion-guided MAPF decomposition lets us scale multi-arm planning by pairing single-arm generators with a collision-resolving duet, beating data-hungry baselines across large teams.
Published in ICRA, 2025
Also presented at NeurIPS Workshop on Intrinsically Motivated Open-Ended Learning ; CoRL Workshop on Learning Effective Abstractions for Planning
Unified conflict-based search with goal-conditioned safe RL to plan long-horizon multi-agent navigation that stays safe without sacrificing efficiency.
Published in IROS, 2024
Extended MAPF with an information-driven heuristic and distributed coordination so multi-robot explorers maximize unique observations even under intermittent communication.
Published in IEEE Transactions on Intelligent Transportation Systems, 2020
Derived a hierarchical Bayesian model that learns accurate bus dwell-time distributions from sparse data, enabling reliable arrival predictions for signal priority systems.
Published in NERC | Extended Abstract, 2024
Leveraged diffusion models to seed decentralized multi-arm planning with high-quality samples, reducing coordination failures in tightly coupled manipulation tasks.
Published in AAAI Spring Symposium on Human-Like Learning (Oral), 2024
Bridged conditional planning with hierarchical RL to iteratively refine risk budgets, enabling agents to tackle long-horizon tasks while respecting safety constraints.
Published in CoRL | Workshop on Learning Effective Abstractions for Planning, 2023
Trained recurrent models that learn search heuristics for multi-agent planners, accelerating long-horizon planning while preserving solution quality.
Published in ICAPS | Workshop on Hierarchical Planning, 2022
Combined timeline-based scheduling with HTNs to coordinate multi-robot teams under resource constraints, yielding resilient plans compared to state-of-the-art temporal planners.
Published in Principles of Social Networking, Springer, 2021
Analyzed how targeted node and edge removals disrupt community structure, introducing heuristics that stay near-optimal even on large-scale social graphs.
Scales Bayesian sequential forecasting to real-time deployments by swapping costly MCMC with Nested Sampling, keeping accuracy high even in small, non-stationary datasets.
Published in RISS Working Papers Journal, 2018
Benchmarked the accuracy limits of DSRC GPS for pedestrian safety applications and fused smartphone and DSRC signals to cut low-speed localization error.