About

My ultimate goal is to have self-sufficient autonomous mobile robots working in human environments, performing tasks accurately and robustly. In support of this goal, I am interested in research in perception, planning, and control applied to autonomous mobile robots. My research in perception involves developing models and representations for a dynamic world, and algorithms to build and perform inference based on such models. My interests in planning include motion planning, multi-robot coordination, and task-based planning in domains including service mobile robots, and robot soccer.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Cartography
• Geography
• Operations research
• Real-time computing
• Simulation
• Programming language
• Data science

Selected publications

• The Essentials of AI for Life and Society: A Full-Scale AI Literacy Course Accessible to All

  Proceedings of the AAAI Conference on Artificial Intelligence · 2026-03-14

  articleOpen access

  In Fall 2023, we introduced a new AI Literacy class called The Essentials of AI for Life and Society (CS 109), a one-credit, seminar course consisting mainly of guest lectures, which was open to the entire university, including students, staff, and faculty. Building on its success and popularity, this paper describes our significant expansion of the course into a full-scale three-credit undergraduate course (CS 309), with an expanded emphasis on student engagement, interactivity, and ethics-related components. To knit together content from the guest lecturers, we implemented a flipped classroom. This model used weekly asynchronous learning modules---integrating pre-recorded expert lectures, collaborative readings, and ethical reflections---which were then unified by the course instructor during a live, interactive discussion session. To maintain the broad accessibility of the material (no prerequisites), the course introduced substantive, non-programming homework assignments in which students applied AI concepts to grounded, real-world problems. This work culminated in a final project analyzing the ethical and societal implications of a chosen AI tool. The redesigned course received overwhelmingly positive student feedback, highlighting its interactivity, coherence, and accessible and engaging assignments. This paper details the course's evolution, its pedagogical structure, and the lessons learned in developing a core AI literacy course. All course materials are freely available for others to use and build upon.

  PublisherOA PDFDOI

• Coadaptive Value Alignment

  2026-05-24

  articleSenior author

  The integration of autonomous agents into human society is a grand challenge for AI. In order to achieve widespread acceptance, agents must conform to the values of people with whom they interact. Current approaches treat the value alignment problem as a unidirectional interaction where the aim is to imbue an agent's actions with human values. Our Coadaptive Value Alignment paradigm acknowledges that human perceptions, expectations, and values continuously evolve in response to agent actions. We conceptualize human-agent interaction as an adaptive loop where the agent actively models and intentionally influences the human's perception, rather than just acting according to static human values. For instance, unlike a traditional agent that simply maximizes speed, an adaptive agent could detect a drop in user trust and strategically sacrifice short-term efficiency to repair the relationship. This perspective transforms value alignment into a multi-agent challenge where all actors must identify and adhere to a shared, implicit social contract. The opportunity to create a virtuous cycle of self-improvement is accompanied by the risk of negative reinforcement, which could result in undesired behaviors. We outline the core framework components, present a research road map for the MAS community, and propose that this dynamic perspective is critical for creating truly collaborative social partners.

  PublisherDOI

• VOFA: Visual Object Goal Pushing with Force-Adaptive Control for Humanoids

  ArXiv.org · 2026-05-02

  articleOpen accessSenior author

  The ability to push large objects in a goal-directed manner using onboard egocentric perception is an essential skill for humanoid robots to perform complex tasks such as material handling in warehouses. To robustly manipulate heavy objects to arbitrary goal configurations, the robot must cope with unknown object mass and ground friction, noisy onboard perception, and actuation errors; all in a real-time feedback loop. Existing solutions either rely on privileged object-state information without onboard perception or lack robustness to variations in goal configurations and object physical properties. In this work, we present VOFA, a visual goal-conditioned humanoid loco-manipulation system capable of pushing objects with unknown physical properties to arbitrary goal positions. VOFA consists of a two-level hierarchical architecture with a high-level visuomotor policy and a low-level force-adaptive whole-body controller. The high-level policy processes noisy onboard observations and generates goal-conditioned commands to operate in closed loop across diverse object-goal configurations, while the low-level whole-body controller provides robustness to variations in object physical properties. VOFA is extensively evaluated in both simulation and real-world experiments on the Booster T1 humanoid robot. Our results demonstrate strong performance, achieving over 90% success in simulation and over 80% success in real-world trials. Moreover, VOFA successfully pushes objects weighing up to 17kg, exceeding half of the Booster T1's body weight.

  PublisherOA PDF

• VOFA: Visual Object Goal Pushing with Force-Adaptive Control for Humanoids

  arXiv (Cornell University) · 2026-05-02

  preprintOpen accessSenior author

  The ability to push large objects in a goal-directed manner using onboard egocentric perception is an essential skill for humanoid robots to perform complex tasks such as material handling in warehouses. To robustly manipulate heavy objects to arbitrary goal configurations, the robot must cope with unknown object mass and ground friction, noisy onboard perception, and actuation errors; all in a real-time feedback loop. Existing solutions either rely on privileged object-state information without onboard perception or lack robustness to variations in goal configurations and object physical properties. In this work, we present VOFA, a visual goal-conditioned humanoid loco-manipulation system capable of pushing objects with unknown physical properties to arbitrary goal positions. VOFA consists of a two-level hierarchical architecture with a high-level visuomotor policy and a low-level force-adaptive whole-body controller. The high-level policy processes noisy onboard observations and generates goal-conditioned commands to operate in closed loop across diverse object-goal configurations, while the low-level whole-body controller provides robustness to variations in object physical properties. VOFA is extensively evaluated in both simulation and real-world experiments on the Booster T1 humanoid robot. Our results demonstrate strong performance, achieving over 90% success in simulation and over 80% success in real-world trials. Moreover, VOFA successfully pushes objects weighing up to 17kg, exceeding half of the Booster T1's body weight.

  PublisherDOI

• GuideNav: User-Informed Development of a Vision-Only Robotic Navigation Assistant for Blind Travelers

  2026-03-10

  article

  While commendable progress has been made in user-centric research on mobile assistive systems for blind and low-vision (BLV) individuals, references that directly inform robot navigation design remain rare. To bridge this gap, we conducted a comprehensive human study involving interviews with 26 guide dog handlers, four white cane users, nine guide dog trainers, and one O&M trainer, along with 15+ hours of observing guide dog–assisted walking. After de-identification, we open-sourced the dataset to promote human-centered development and informed decision-making for assistive systems for BLV people. Building on insights from this formative study, we developed GuideNav, a vision-only, teach-and-repeat navigation system. Inspired by how guide dogs are trained and assist their handlers, GuideNav autonomously repeats a path demonstrated by a sighted person using a robot. Specifically, the system constructs a topological representation of the taught route, integrates visual place recognition with temporal filtering, and employs a relative pose estimator to compute navigation actions---all without relying on costly, heavy, power-hungry sensors such as LiDAR. In field tests, GuideNav consistently achieved kilometer-scale route following across five outdoor environments, maintaining reliability despite noticeable scene variations between teach and repeat runs. A user study with 3 guide dog handlers and 1 guide dog trainer further confirmed the system’s feasibility, marking, to our knowledge, the first demonstration of a quadruped mobile system guiding a route in a manner comparable to guide dogs.

  PublisherDOI

• Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds

  2025-10-19

  articleSenior author

  Social robot navigation in crowded public spaces such as university campuses, restaurants, grocery stores, and hospitals, is an increasingly important area of research. One of the core strategies for achieving this goal is to understand humans’ intent–underlying psychological factors that govern their motion–by learning how humans assign rewards to their actions, typically via inverse reinforcement learning (IRL). Despite significant progress in IRL, learning reward functions of multiple agents simultaneously in dense unstructured pedestrian crowds has remained intractable due to the nature of the tightly coupled social interactions that occur in these scenarios e.g. passing, intersections, swerving, weaving, etc. In this paper, we present a new multi-agent maximum entropy inverse reinforcement learning algorithm for real world unstructured pedestrian crowds. Key to our approach is a simple, but effective, mathematical trick which we name the so-called "tractability-rationality trade-off" trick that achieves tractability at the cost of a slight reduction in accuracy. We compare our approach to the classical single-agent MaxEnt IRL as well as state-of-the-art trajectory prediction methods on several datasets including the ETH, UCY, SCAND, JRDB, and a new dataset, called Speedway, collected at a busy intersection on a University campus focusing on dense, complex agent interactions. Our key findings show that, on the dense Speedway dataset, our approach ranks 1<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> among top 7 baselines with > 2× improvement over single-agent IRL, and is competitive with state-of-the-art large transformer-based encoder-decoder models on sparser datasets such as ETH/UCY (ranks 3<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> among top 7 baselines).

  PublisherDOI

• ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments

  2025-10-19 · 1 citations

  article

  The growing use of service robots in dynamic environments requires flexible management of on-board compute resources to optimize the performance of diverse tasks such as navigation, localization, and perception. Current robot deployments often rely on static OS configurations and system over-provisioning. However, they are suboptimal because they ignore variations in resource usage, leading to system-wide issues like robot instability or inefficient resource utilization. This paper presents ConfigBot, a novel system designed to adaptively reconfigure robot applications to meet a predefined performance specification by leveraging runtime profiling and automated configuration tuning. Through experiments on multiple real robots, each running a different stack with diverse performance requirements, which could be context-dependent, we illustrate ConfigBot's efficacy in maintaining system stability and optimizing resource allocation. Our findings highlight the promise of automatic system configuration tuning for robot deployments, including adaptation to dynamic changes. Code available at: https://github.com/ldos-project/configbot

  PublisherDOI

• Terrain Costmap Generation via Scaled Preference Conditioning

  ArXiv.org · 2025-11-14

  preprintOpen accessSenior author

  Successful autonomous robot navigation in off-road domains requires the ability to generate high-quality terrain costmaps that are able to both generalize well over a wide variety of terrains and rapidly adapt relative costs at test time to meet mission-specific needs. Existing approaches for costmap generation allow for either rapid test-time adaptation of relative costs (e.g., semantic segmentation methods) or generalization to new terrain types (e.g., representation learning methods), but not both. In this work, we present scaled preference conditioned all-terrain costmap generation (SPACER), a novel approach for generating terrain costmaps that leverages synthetic data during training in order to generalize well to new terrains, and allows for rapid test-time adaptation of relative costs by conditioning on a user-specified scaled preference context. Using large-scale aerial maps, we provide empirical evidence that SPACER outperforms other approaches at generating costmaps for terrain navigation, with the lowest measured regret across varied preferences in five of seven environments for global path planning.

  PublisherOA PDFDOI

• CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance

  2025-06-21 · 1 citations

  articleOpen access

  Fig. 1: CRESTE learns a perceptual encoder 1,2 and reward function 3 to predict structured bird's eye view (BEV) feature and reward maps for navigation.Our model inherits generalization and robustness from visual foundation models and learns expert-aligned rewards using our counterfactual-guided learning framework.We integrate CRESTE in a modular navigation system that uses coarse GPS guidance and rewards to reach navigation goals safely.

  PublisherDOI

• Human-Centered Development of Guide Dog Robots: Quiet and Stable Locomotion Control

  ArXiv.org · 2025-05-17

  preprintOpen access

  A quadruped robot is a promising system that can offer assistance comparable to that of dog guides due to its similar form factor. However, various challenges remain in making these robots a reliable option for blind and low-vision (BLV) individuals. Among these challenges, noise and jerky motion during walking are critical drawbacks of existing quadruped robots. While these issues have largely been overlooked in guide dog robot research, our interviews with guide dog handlers and trainers revealed that acoustic and physical disturbances can be particularly disruptive for BLV individuals, who rely heavily on environmental sounds for navigation. To address these issues, we developed a novel walking controller for slow stepping and smooth foot swing/contact while maintaining human walking speed, as well as robust and stable balance control. The controller integrates with a perception system to facilitate locomotion over non-flat terrains, such as stairs. Our controller was extensively tested on the Unitree Go1 robot and, when compared with other control methods, demonstrated significant noise reduction -- half of the default locomotion controller. In this study, we adopt a mixed-methods approach to evaluate its usability with BLV individuals. In our indoor walking experiments, participants compared our controller to the robot's default controller. Results demonstrated superior acceptance of our controller, highlighting its potential to improve the user experience of guide dog robots. Video demonstration (best viewed with audio) available at: https://youtu.be/8-pz_8Hqe6s.

  PublisherOA PDFDOI

Recent grants

• CAREER: Robust Perception and Customization for Long-Term Autonomous Mobile Service Robots

  NSF · $590k · 2021–2027

• Collaborative Research: RI: Medium: Introspective Perception and Planning for Long-Term Autonomy

  NSF · $600k · 2020–2024

• Collaborative Research: SHF: Small: Interactive Synthesis and Repair For Robot Programs

  NSF · $250k · 2020–2023

Frequent coauthors

• Sadegh Rabiee

  The University of Texas at Austin

  28 shared

• Peter Stone

  23 shared

• Manuela Veloso

  23 shared

• Arjun Guha

  Northeastern University

  19 shared

• Jarrett Holtz

  Robert Bosch (United States)

  18 shared

• Haresh Karnan

  17 shared

• Xuesu Xiao

  16 shared

• Garrett Warnell

  15 shared

Labs

• Autonomous Mobile Robotics Laboratory (AMRL)PI

  Research in perception, planning, and control applied to autonomous mobile robots

Education

• PhD, Robotics Institute

  Carnegie Mellon University

  2014

Awards & honors

• NSF Research Traineeship Award
• NSF CAREER Award
• NSF Award
• J.P. Morgan Faculty Research Award
• Amazon Research Award