About

Nancy M. Amato is the Abel Bliss Professor of Engineering and Director of the Siebel School of Computing and Data Science within the Grainger College of Engineering at the University of Illinois Urbana-Champaign. She holds undergraduate degrees in Mathematical Sciences and Economics from Stanford University, and M.S. and Ph.D. degrees in Computer Science from UC Berkeley and the University of Illinois, respectively. Before returning to her alma mater in 2019, she was the Unocal Professor and Regents Professor at Texas A&M University and served as Senior Director of Engineering Honors Programs. Her research encompasses algorithmic contributions to robotics task and motion planning, computational biology and geometry, and parallel and distributed computing. She has graduated 29 PhD students, many of whom have gone on to faculty or research positions, and has mentored over 100 undergraduate researchers and high school students, with a focus on increasing diversity in computing. Her group has developed groundbreaking approaches for biasing sampling in sampling-based motion planning, enabling applications in areas such as protein motion and folding, and has made significant contributions to computational geometry and parallel algorithms. Her current research explores task planning and interaction in dynamic multi-robot systems, protein-drug interactions, and methods that incorporate learning and workspace topology. She also collaborates on projects related to parallel algorithms for large graphs and is co-leading a multidisciplinary NSF project aimed at building computing systems based on living biological neurons. Amato has held numerous leadership roles, including CRA Board Chair, IEEE Robotics and Automation Society President, ACM Council Member-at-Large, and AAAS Section-T Chair. She is passionate about broadening participation in computing, having served as CRA-WP Co-Chair, NCWIT Academic Alliance Co-Chair, and as co-director of the CRA-WP Distributed REU program. Her honors include awards from IEEE, NCWIT, CRA, and the University of Illinois, as well as fellowships with AAAI, AAAS, ACM, and IEEE, and membership in the American Academy of Arts and Sciences.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Computational science
• Human–computer interaction
• Algorithm
• Data science
• Optics
• Engineering
• Geometry
• Theoretical computer science
• Computer vision
• Physics
• World Wide Web
• Mathematical optimization
• Parallel computing

Selected publications

• Quick Heuristic Validation of Edges in Dynamic Roadmap Graphs

  Open MIND · 2026-01-28

  preprintSenior author

  In this paper we tackle the problem of adjusting roadmap graphs for robot motion planning to non-static environments. We introduce the "Red-Green-Gray" paradigm, a modification of the SPITE method, capable of classifying the validity status of nodes and edges using cheap heuristic checks, allowing fast semi-lazy roadmap updates. Given a roadmap, we use simple computational geometry methods to approximate the swept volumes of robots and perform lazy collision checks, and label a subset of the edges as invalid (red), valid (green), or unknown (gray). We present preliminary experimental results comparing our method to the well-established technique of Leven and Hutchinson, and showing increased accuracy as well as the ability to correctly label edges as invalid while maintaining comparable update runtimes.

  DOI

• Serialized Red-Green-Gray: Quicker Heuristic Validation of Edges in Dynamic Roadmap Graphs

  arXiv (Cornell University) · 2026-03-30

  preprintOpen accessSenior author

  Motion planning in dynamic environments, such as robotic warehouses, requires fast adaptation to frequent changes in obstacle poses. Traditional roadmap-based methods struggle in such settings, relying on inefficient reconstruction of a roadmap or expensive collision detection to update the existing roadmap. To address these challenges we introduce the Red-Green-Gray (RGG) framework, a method that builds on SPITE to quickly classify roadmap edges as invalid (red), valid (green), or uncertain (gray) using conservative geometric approximations. Serial RGG provides a high-performance variant leveraging batch serialization and vectorization to enable efficient GPU acceleration. Empirical results demonstrate that while RGG effectively reduces the number of unknown edges requiring full validation, SerRGG achieves a 2-9x speedup compared to the sequential implementation. This combination of geometric precision and computational speed makes SerRGG highly effective for time-critical robotic applications.

  PublisherDOI

• Multi-Robot Motions in Milliseconds: Vector-Accelerated Primitives for Sampling-Based Planning

  ArXiv.org · 2026-04-27

  articleOpen accessSenior author

  In this paper, we extend the recent Vector-Accelerated Motion Planning (VAMP) framework to multi-robot motion planning (MRMP). We develop two vector-accelerated primitives, multi-robot MotionValidation (MotVal) and FindFirstConflict (FFC), which exploit SIMD parallelism within the multi-robot domain. On pure multi-robot motion validation tests, this achieves over 1100X speedup in validation time. Additionally, we modify a representative set of MRMP algorithms to use these new primitives. The relative speedup for each algorithm is studied on scenarios with manipulator, rigid body, and heterogeneous teams with some instances producing multi-robot solutions in the order of milliseconds and, in many cases, shows planning time speedups of over 850X.

  PublisherOA PDF

• Scalable Multi-Robot Motion Planning Using Workspace Guidance-Informed Hypergraphs

  IEEE Robotics and Automation Letters · 2026-02-19

  articleOpen accessSenior author

  In this work, we propose a method for multiple mobile robot motion planning that efficiently plans for robot teams up to 128 robots (an order of magnitude larger than existing state-of-the-art methods) in congested settings with narrow passages in the environment. We achieve this improvement in scalability by extending the state-of-the-art Decomposable State Space Hypergraph (DaSH)multi-robot planning framework to support mobile robot motion planning in congested environments. This is a problem that DaSH cannot be directly applied to because it lacks a highly structured, easily discretizable task space and features kinodynamic constraints. We accomplish this by exploiting knowledge about the workspace topology to limit exploration of the planning space and through modifying DaSH's conflict resolution scheme. This guidance captures when coordination between robots is necessary, allowing us to decompose the intractably large multi-robot search space while limiting risk of inter-robot conflicts by composing relevant robot groups together while planning.

  PublisherDOI

• Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement

  ArXiv.org · 2026-05-19

  articleOpen accessSenior author

  A fundamental challenge in multi-robot motion planning is achieving sufficient coordination to avoid inter-robot conflicts without incurring the large computational expense of searching the joint configuration space of the robot group. In this work, we present a method for multiple mobile robot motion planning that achieves an improvement in planning time up to an order of magnitude by leveraging the insight that we can use discrete search over a workspace decomposition to provide coordination between robots during planning. While prior work uses workspace topology to inform when coordination between robots is needed and then composes robots into their joint configuration space, we take a step further by iteratively refining our workspace representation to allow our planner to search smaller, decoupled configuration spaces.

  PublisherOA PDF

• Edge Nearest Neighbor: Neighbor-Finding Revisited in Sampling-Based Motion Planning

  IEEE Transactions on Robotics · 2026-01-01

  articleOpen access

  Neighborhood finders and nearest neighbor queries are fundamental components of sampling-based motion planning (SBMP) algorithms. Using different distance metrics or otherwise changing the definition of a neighborhood produces different algorithms with unique empirical and theoretical properties. In his textbook on planning algorithms, LaValle suggests a neighborhood finder for the Rapidly-exploring Random Tree (RRT) algorithm, which finds the nearest neighbor of the sampled point on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">swath</i> of the tree, that is, on the set of all of the points on the tree edges, using a hierarchical data structure. In this paper, we implement such a neighborhood finder and show, theoretically and experimentally, that this results in more efficient algorithms.

  PublisherDOI

• From Legible to Inscrutable Trajectories: (Il)legible Motion Planning Accounting for Multiple Observers

  Open MIND · 2026-02-09

  preprint

  In cooperative environments, such as in factories or assistive scenarios, it is important for a robot to communicate its intentions to observers, who could be either other humans or robots. A legible trajectory allows an observer to quickly and accurately predict an agent's intention. In adversarial environments, such as in military operations or games, it is important for a robot to not communicate its intentions to observers. An illegible trajectory leads an observer to incorrectly predict the agent's intention or delays when an observer is able to make a correct prediction about the agent's intention. However, in some environments there are multiple observers, each of whom may be able to see only part of the environment, and each of whom may have different motives. In this work, we introduce the Mixed-Motive Limited-Observability Legible Motion Planning (MMLO-LMP) problem, which requires a motion planner to generate a trajectory that is legible to observers with positive motives and illegible to observers with negative motives while also considering the visibility limitations of each observer. We highlight multiple strategies an agent can take while still achieving the problem objective. We also present DUBIOUS, a trajectory optimizer that solves MMLO-LMP. Our results show that DUBIOUS can generate trajectories that balance legibility with the motives and limited visibility regions of the observers. Future work includes many variations of MMLO-LMP, including moving observers and observer teaming.

  DOI

• Latent Adversarial Regularization for Offline Preference Optimization

  ArXiv.org · 2026-01-29

  articleOpen access

  Learning from human feedback typically relies on preference optimization that constrains policy updates through token-level regularization. However, preference optimization for language models is particularly challenging because token-space similarity does not imply semantic or behavioral similarity. To address this challenge, we leverage latent-space regularization for language model preference optimization. We introduce GANPO, which achieves latent-space regularization by penalizing divergence between the internal representations of a policy model and a reference model. Given that latent representations are not associated with explicit probability densities, we adopt an adversarial approach inspired by GANs to minimize latent-space divergence. We integrate GANPO as a regularizer into existing offline preference optimization objectives. Experiments across multiple model architectures and tasks show consistent improvements from latent-space regularization. Further, by comparing GANPO-induced inferential biases with those from token-level regularization, we find that GANPO provides more robust structural feedback under distributional shift and noise while maintaining comparable downstream performance with minor computational overhead.

  PublisherOA PDF

• Multi-Robot Motions in Milliseconds: Vector-Accelerated Primitives for Sampling-Based Planning

  arXiv (Cornell University) · 2026-04-27

  preprintOpen accessSenior author

  In this paper, we extend the recent Vector-Accelerated Motion Planning (VAMP) framework to multi-robot motion planning (MRMP). We develop two vector-accelerated primitives, multi-robot MotionValidation (MotVal) and FindFirstConflict (FFC), which exploit SIMD parallelism within the multi-robot domain. On pure multi-robot motion validation tests, this achieves over 1100X speedup in validation time. Additionally, we modify a representative set of MRMP algorithms to use these new primitives. The relative speedup for each algorithm is studied on scenarios with manipulator, rigid body, and heterogeneous teams with some instances producing multi-robot solutions in the order of milliseconds and, in many cases, shows planning time speedups of over 850X.

  PublisherDOI

• Latent Adversarial Regularization for Offline Preference Optimization

  Open MIND · 2026-01-29

  preprint

  Learning from human feedback typically relies on preference optimization that constrains policy updates through token-level regularization. However, preference optimization for language models is particularly challenging because token-space similarity does not imply semantic or behavioral similarity. To address this challenge, we leverage latent-space regularization for language model preference optimization. We introduce GANPO, which achieves latent-space regularization by penalizing divergence between the internal representations of a policy model and a reference model. Given that latent representations are not associated with explicit probability densities, we adopt an adversarial approach inspired by GANs to minimize latent-space divergence. We integrate GANPO as a regularizer into existing offline preference optimization objectives. Experiments across multiple model architectures and tasks show consistent improvements from latent-space regularization. Further, by comparing GANPO-induced inferential biases with those from token-level regularization, we find that GANPO provides more robust structural feedback under distributional shift and noise while maintaining comparable downstream performance with minor computational overhead.

  DOI

Recent grants

• DC: Small: Collaborative Research: Shape Representation of Large Geometries via Convex Approximation

  NSF · $232k · 2009–2015

• Motion-Planning Based Techniques for Modeling & Simulating Molecular Motions

  NSF · $434k · 2008–2014

• AF: Small: Motion Planning Techniques for Protein Motion

  NSF · $208k · 2019–2020

• RI: Small: Scalable Roadmap-Based Methods for Simulating and Controlling Behaviors of Interacting Groups: from Robot Swarms to Crowd Control

  NSF · $504k · 2009–2015

• AF: Small: Motion Planning Techniques for Protein Motion

  NSF · $432k · 2014–2019

Frequent coauthors

• Shawna Thomas

  Mitchell Institute

  64 shared

• Lawrence Rauchwerger

  44 shared

• Marco Morales

  University of Illinois Urbana-Champaign

  41 shared

• Guang Song

  26 shared

• Jyh‐Ming Lien

  Film Independent

  24 shared

• Jory Denny

  21 shared

• O. Burçhan Bayazıt

  Washington University in St. Louis

  21 shared

• James Motes

  20 shared

Education

• Ph.D., Computer Science

  University of California, Santa Barbara

  1992

• M.S., Computer Science

  University of California, Santa Barbara

  1987

• B.S., Computer Science

  University of California, Santa Barbara

  1985

Awards & honors

• Distinguished CS Alumnus Award from UC Berkeley (2024)
• MassRobotics Robotics Medal (2023)
• IEEE RAS Saridis Leadership Award in Robotics and Automation…
• NCWIT Harrold and Notkin Research and Graduate Mentoring Awa…
• A. Nico Habermann Award from the CRA (2014)