Mac Schwager

About

Mac Schwager is an Associate Professor of Aeronautics and Astronautics at Stanford University, with a courtesy appointment in Computer Science. His research focuses on autonomous systems, controls, and their applications in aerospace and transportation. As a faculty member at Stanford's Department of Aeronautics and Astronautics, he contributes to advancing the understanding and development of intelligent systems that can operate independently in complex environments. His work is integral to the department's efforts in autonomous systems and controls, supporting innovations in future aircraft design, space exploration, and transportation technologies.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematical optimization
• Mathematics
• Algorithm
• Mathematical economics
• Mathematical analysis
• Control engineering
• Distributed computing
• Physics

Selected publications

• An untethered isoperimetric soft robot

  Science Robotics · 2020 · 144 citations

  For robots to be useful for real-world applications, they must be safe around humans, be adaptable to their environment, and operate in an untethered manner. Soft robots could potentially meet these requirements; however, existing soft robotic architectures are limited by their ability to scale to human sizes and operate at these scales without a tether to transmit power or pressurized air from an external source. Here, we report an untethered, inflated robotic truss, composed of thin-walled inf…

  DOI

• Scalable Cooperative Transport of Cable-Suspended Loads With UAVs Using Distributed Trajectory Optimization

  IEEE Robotics and Automation Letters · 2020 · 82 citations

  Most approaches to multi-robot control either rely on local decentralized control policies that scale well in the number of agents, or on centralized methods that can handle constraints and produce rich system-level behavior, but are typically computationally expensive and scale poorly in the number of agents, relegating them to offline planning. This work presents a scalable approach that uses distributed trajectory optimization to parallelize computation over a group of computationally-limited…

  DOI

• Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions

  IEEE Transactions on Robotics · 2023 · 45 citations

  Senior authorCorresponding

  In this article, we study the problem of multiple stochastic agents interacting in a dynamic game scenario with continuous state and action spaces. We define a new notion of stochastic Nash equilibrium for boundedly rational agents, which we call the entropic cost equilibrium (ECE). We show that ECE is a natural extension to multiple agents of maximum entropy optimality for a single agent. We solve both the “forward” and “inverse” problems for the multi-agent ECE game. For the forward problem, w…

  DOI

• Game-Theoretic Planning for Risk-Aware Interactive Agents

  2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2020 · 35 citations

  Senior authorCorresponding

  Modeling the stochastic behavior of interacting agents is key for safe motion planning. In this paper, we study the interaction of risk-aware agents in a game-theoretical framework. Under the entropic risk measure, we derive an iterative algorithm for approximating the intractable feedback Nash equilibria of a risk-sensitive dynamic game. We use an iteratively linearized approximation of the system dynamics and a quadratic approximation of the cost function in solving a backward recursion for fi…

  DOI

• Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps

  IEEE Transactions on Robotics · 2025-01-01 · 22 citations

  articleSenior author

  We present Splat-Nav, a real-time robot navigation pipeline for Gaussian splatting (GSplat) scenes, a powerful new 3-D scene representation. Splat-Nav consists of two components: first, Splat-Plan, a safe planning module, and second, Splat-Loc, a robust vision-based pose estimation module. Splat-Plan builds a safe-by-construction polytope corridor through the map based on mathematically rigorous collision constraints and then constructs a Bézier curve trajectory through this corridor. Splat-Loc…

  PublisherDOI

Recent grants

• CAREER: Controlling Ecologically Destructive Processes with a Network of Intelligent Robotic Agents

  NSF · $317k · 2016–2019

• NRI: FND: COLLAB: Distributed Semantically-Aware Tracking and Planning for Fleets of Robots

  NSF · $468k · 2018–2022

• CAREER: Controlling Ecologically Destructive Processes with a Network of Intelligent Robotic Agents

  NSF · $286k · 2014–2016

Frequent coauthors

• Daniela Rus

  75 shared

• Eduardo Montijano

  Universidad de Zaragoza

  63 shared

• Zijian Wang

  Shenyang Ligong University

  61 shared

• Eric Cristofalo

  52 shared

• Riccardo Spica

  Vaughn College of Aeronautics and Technology

  36 shared

• Ola Shorinwa

  33 shared

• Davide Scaramuzza

  32 shared

• Haruki Nishimura

  30 shared

Education

• Ph.D., Aeronautics and Astronautics

  Stanford University

  2005

• M.S., Aeronautics and Astronautics

  Stanford University

  2001

• B.S., Aeronautics and Astronautics

  California Institute of Technology

  1998

Awards & honors

• AIAA: Excellence in Teaching Award
• AIAA: Outstanding Course Assistant
• William F. Ballhaus Prize
• Cannon Summer Fellowship
• Hoff Outstanding Master’s Student

Similar researchers at Stanford University

• Charbel Farhath-89
• Marco Pavoneh-67
• Juan Alonsoh-52