linkedin gscholar youtube github

I am a roboticist who is passionate about developing planning and control algorithms that enable robots to fully leverage their dynamic capabilities and operate with or with better than ``human-level" ability in real-world environments.

On the technical side, I am interested in challenges associated with complex vehicle dynamics—under-actuation, high-dimensionality, nonlinearity; interaction with the environment—avoiding obstacles, planning through contact, cooperating with other robots; and safe deployment on real-world platforms—planning with onboard sensing and computation constraints, formulating formal performance guarantees. I believe that through combining model-based tools such as geometric control, hybrid systems theory, computational motion planning, and optimization with machine learning and deep learning techniques, we can create new methods that offer safety, completeness, and/or (sub-) optimality guarantees while also not restricting robots to overly conservative actions.

On the application side, I am excited about the potential of robots to improve the infrastructure of the cities of the future. In past lives, I have worked on aerial robots for construction and package delivery at the Vijay Kumar Lab housed under the UPenn General Robotics, Automation, Sensing & Perception (GRASP) Lab, robo-taxis at Waymo/Google, ground robots for mapping and surveyance at the Carnegie Mellon University Robotics Institute, and underwater robots for enviornmental study at Princeton University.

I am currently working in the San Francisco Bay Area as a Robotics Software Engineer at Nuro, building autonomous vehicles for local goods transportation.

In my spare time, I like to hike and take pictures.

Learning for unlabeled multi-robot coordination

We present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstacle-filled workspaces. More specifically, we tackle the unlabeled multi-robot motion planning problem with motion constraints as a multi-agent reinforcement learning problem with some sparse global reward. In contrast with previous works, our framework is a general approach that is applicable to arbitrary robot models.

pdf icon bibtex icon

Arbaaz Khan, Chi Zhang, Shuo Li, Jiayue Wu, Brent Schlotfeldt, Sarah Tang, Alejandro Ribeiro, Osbert Bastani, and Vijay Kumar. “Learning safe unlabeled multi-robot planning with motion constraints”. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Macau. Nov, 2019.


In December 2018, I graduated with my PhD in Mechanical Engineering and Applied Mechanics from the UPenn GRASP Lab!

pdf icon bibtex icon

Sarah Tang. "Control, planning, and coordination for dynamic aerial manipulation with robot teams". 2018. Publicly Accessible Penn Dissertations. 3191.

MIQP trajectory planning for quadrotors with cable-suspended payloads

In this work, we propose an optimization-based trajectory planning algorithm that allows for incorporation of large, yet controlled, payload swings when appropriate. We demonstrate two novel capabilities: maneuvering through difficult obstacles, such as windows whose height is smaller than the cable length, and rapid payload pick-up and releases without the quadrotor moving directly above the payload. This is the first experimental demonstration of these types of maneuvers, which could enable automate rapid Christmas tree harvesting and delivery to hard-to-access disaster sites.

pdf icon bibtex icon

Sarah Tang and Vijay Kumar. “Mixed integer quadratic program trajectory generation for a quadrotor with a cable-suspended payload”. IEEE International Conference on Robotics and Automation (ICRA). Seattle, WA. May 2015.

pdf icon bibtex icon

Sarah Tang and Vijay Kumar. “Planning aggressive maneuvers for a quadrotor with a cable-suspended payload.” IEEE International Conference on Robotics and Automation (ICRA), Becoming a Robot Guru: Integrating Science, Engineering, and Creativity Workshop. Seattle, WA. May 30, 2015. Poster presentation.

pdf icon bibtex icon

Sarah Tang, Koushil Sreenath, and Vijay Kumar. “Aggressive maneuvering of a quadrotor with a cable-suspended payload”. Robotics: Science and Systems (RSS), Workshop on Women in Robotics. Berkeley, CA. July 12, 2014. Poster presentation.

pdf icon bibtex icon

Sarah Tang. “Aggressive maneuvering of a quadrotor with a cable-suspended payload". University of Pennsylvania Qualification Report, 2014.

Vision-based geometric control of suspended payloads

Geometric controllers can theortically guarantee stable control of the quadrotor-with-suspended-payload system through configurations where the payload is swung far from the vertical. However, this control approach has not yet been realized in practice. In this work, by using data from a downward facing camera and an onboard IMU, we are able to accurately estimate the payload state and utilize a geometric controller to robustly control agile maneuvers that include payload swings of up to 50 degrees from the vertical. This is the first experimental demonstration of closed-loop feedback control in the three-dimensional workspace for this system and represents a step towards automated payload maneuvering in real-world applications.

pdf icon bibtex icon

Sarah Tang*, Valentin Wüest*, and Vijay Kumar. (*Equal contribution.) “Aggressive flight with suspended payloads using vision-based control”. Robotics and Automation Letters (RA-L), vol. 3, no. 2, pp. 1152—1159, Apr. 2018. doi: 10.1109/LRA.2018.2793305. To be presented at IEEE International COnference on Robotics and Automation (ICRA) 2018. Brisbane, Australia. May 2018.

This paper was nominated for the IEEE ICRA Best Paper Award on Unmanned Aerial Vehicles.

Automated payload delivery with quadrotor teams

Most multi-robot systems currently used consist of approximately first- or second-order vehicles. Instead, we consider a team of quadrotors, each carrying a cable-suspended payload, and develop an algorithm for safe simultaneous navigation of all payloads to designated goal positions. We do not constrain the payloads to remain vertical, but instead, allow them to swing upwards when it is safe to do so. This work is the first demonstration of a multi-robot team with vehicles of this level of complexity and is applicable to tasks such as construction, where a single crane performing sequential tasks can be replaced by multiple quadrotors.

pdf icon bibtex icon

Sarah Tang, Koushil Sreenath, and Vijay Kumar. "Multi-robot trajectory generation for an aerial payload delivery system". In International Symposium on Robotics Research (ISRR). Puerto Varas, Chile. Dec. 2017.

Scalable multi-robot trajectory generation with QPs

Trajectory planning for multi-robot teams is a difficult problem --- the problem's search space grows exponentially with the number of robots and each robot often contains kinemtic or dynamic constraints that its trajectory must satisfy. In this light, we propose a two-step algorithm for multi-robot planning. First, a fast (though sub-optimal) motion planning algorithm designs local roundabout-like collision avoidance maneuvers to navigate all robots safely to designated goal positions. Then, a trajectory smoothing process generates a safe, dynamically feasible trajectory for each robot. Unlike past works that optimize all robots’ trajectories in a single optimization problem, our algorithm constructs an independent optimization problem for each vehicle, giving it polynomial, instead of exponential, computation time complexity with respect to the number of robots. We show that this method can safely navigate quadrotors in close proximities.

pdf icon bibtex icon

Sarah Tang, Justin Thomas, and Vijay Kumar. “Hold or take optimal plan (HOOP): a quadratic programming approach to multi-robot trajectory generation”. International Journal of Robotics Research (IJRR). In press.

pdf icon bibtex icon

Sarah Tang, Justin Thomas, and Vijay Kumar. “Safe navigation of quadrotor teams to labeled goals in limited workspaces”. International Symposium on Experimental Robotics (ISER). Tokyo, Japan. Oct. 2016.

pdf icon bibtex icon

Sarah Tang and Vijay Kumar. “Safe and complete trajectory generation for robot teams with higher-order dynamics”. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Daejeon, Korea. Oct. 2016.

pdf icon bibtex icon

Sarah Tang and Vijay Kumar. “A complete algorithm for generating safe trajectories for multi-robot teams". International Symposium on Robotics Research (ISRR). Sestri Levante, Italy. Sept. 2015.

pdf icon bibtex icon

Sarah Tang and Vijay Kumar. “Translating paths into optimal trajectories for safe coordination of teams of dynamic robots.” Robotics: Science and Systems (RSS), Workshop on On-line Decision-Making in Multi-robot Coordination. Ann Arbor, Michigan. June 19, 2016. Oral presentation.

Real-time planning with an onboard RGB-D sensor

Bringing robots out of motion-capture arenas and into real-world environments is a challenging task that requires interfacing planning algorithms with onboard perception and mapping algorithms. In this work, we propose a method for finding safe, dynamically feasible trajectories through an occupancy grid map built using an onboard RGB-D sensor and demonstrate the execution of the complete obstacle detection to mapping to trajectory execution pipeline on a quadrotor.

pdf icon bibtex icon

Sikang Liu, Michael Watterson, Sarah Tang, and Vijay Kumar. “High speed navigation for quadrotors with limited onboard sensing”. IEEE International Conference on Robotics and Automation (ICRA). Stockholm, Sweden. May 2016.

Market-based task allocation for cooperative mapping with heterogenous ground robots

Deploying robots into unknown regions to create maps is a common application for ground robots. Using a heterogenous team for this task allows for mapping across different types of terrains and data collection from different sensors. In this work, we describe a framework for autonomous exploration using such a team and demonstrate its usage in modeling a rugged tunnel enviornment.

pdf icon bibtex icon

Ammar Husain, Heather Jones, Balajee Kannan, Uland Wong, Tiago Pimentel, Sarah Tang, Shreyansh Daftry, Steven Huber, and William L. “Red" Whittaker. “Mapping planetary caves with an autonomous, heterogeneous robot team". IEEE Aerospace Conference. Big Sky, MT. Mar. 2013.

Decentralized control for shark tracking with a team of autonomous underwater robots

Collecting data on the behavior of long migratory fish species is crucial to the field of marine biology. Unfortunately, current methods for fish tracking suffer significant drawbacks. Satellite tags are able to track fish across long distances, however, can only gather measurements when the fish are at the water’s surface. Acoustic tags are able to function underwater, but require researchers to manually track and follow the fish on boats, which is labor intensive and unsustainable over large distances. Autonomous Underwater Vehicles (AUVs) could potentially solve both these challenges. They can be equipped with acoustic receivers for accurate tracking and have long battery lives for continuous data collection. In this work, we develop a control mechanism that allows a team of AUVs to follow and track a leopard shark.

pdf icon bibtex icon

Dylan Shinzaki, Chris Gage, Sarah Tang, Mark A. Moline, Barrett Wolfe, Christopher G. Lowe, and Christopher M. Clark. “A multi-AUV system for cooperative tracking and following of leopard sharks". IEEE International Conference on Robotics and Automation (ICRA). Karlsruhe, Germany. May 2013.

pdf icon bibtex icon

Sarah Tang, Dylan Shinzaki, Chris G. Lowe, and Chris M. Clark. “Multi-robot control for circumnavigation of particle distributions". International Symposium on Distributed Autonomous Robotic Systems (DARS). Baltimore, MD. Nov. 2012.

Benchmarking 3D descriptors for shape retrieval

3D shape retrieval is the process of identifying the subset of 3D models in a collection that are the same as a given query. This problem is challenging, as models can be in different poses or represented from different viewing angles. Many works have focused on developing shape descriptors, scalar or vector quantities calculated at chosen points on the meshes, to determine two models’ similarity. However, proposed descriptors are often evaluated independently and on different datasets. This paper systematically compares a number of prominent 3D shape descriptors and benchmarks their performance.

pdf icon bibtex icon

Sarah Tang and Afzal Godil. “An evaluation of local shape descriptors for 3D shape retrieval". IS&T/SPIE Electronic Imagining, Three-Dimensional Image Processing (3DIP) and Applications II. Burlingame, CA. Jan. 2012.

pdf icon bibtex icon

Sarah Tang. “An evaluation of local shape descriptors for 3D shape retrieval". NIST Interagency/Internal Report (NISTIR) 7812. Mar. 2012.

UAV Learning Workshop at ICRA 2019

I co-organized a workshop on Algorithms and Architectures for Learning-in-the-Loop Systems" at ICRA 2019. Thank you to everyone who attended, especially our wonderful invited speakers!

pdf icon news icon

“Algorithms and Architectures for Learning-in-the-Loop Systems in Autonomous Flight".IEEE International Conference on Robotics and Automation Full-Day Workshop. Montreal, Canada, May 24, 2019.

Research talks at Google Brain and Nvidia

Thank you to Google and Nvidia for allowing me to share my research, "Dynamic manipulation with aerial robot teams"!

pdf icon bibtex icon

“Dynamic manipulation with aerial robot teams". Google Brain. Mountain View, CA, Feb. 28, 2018. Nvidia. Seattle, WA, Mar. 1, 2018.

University of Pennsylvania iTalks

In 2015, I participated in Penn’s iTalks competition, an event where participants gave 12-minute TED-talk style presentations that were judged by a faculty panel and the student audience on clarity of presentation, impact of work, and interdisciplinary nature of research. This talk won the Best Presentation (determined by faculty judges) and the Audience Favorite (determined by audience voting) Awards.

pdf icon news icon

“Planning for aggressive maneuvers for a quadrotor with a cable-suspended load". Interdisciplinary Talks (iTalks) Competition".University of Pennsylvania. Philadelphia, PA. Apr. 1, 2015.

Princeton University Center for Information Technology Policy panel

"This talk is the second in our “Can Law Keep Up with New Technology?” series of lunch timers. Each program explores the current state of an emerging technology and the legal and ethical considerations that stem from it. Peter Asaro and Sarah Tang will discuss non-military drones: what is possible now and in the near future using drone technology and how we should think about their effect on privacy in public space, considering surveillance and remote sensing capabilities."

pdf icon news icon

“Lunch Timer with Peter Asaro and Sarah Tang – Non-Military Drones: What Laws and Ethics Do We Need?"Princeton University, Center for Information Technology Policy. Princeton, NJ. Mar. 30, 2015.

Tutorial talk at the Technology Management Conference at Saab Dynamics

pdf icon bibtex icon

“Quadrotor swarms: hardware, algorithms, and applications". Annual Technology Management Conference at Saab Dynamics. With Philip Dames. Karlskoga, Sweden, Feb. 4, 2015. Linköping, Sweden, Feb. 5, 2015.

Drones learn tricks with suspended loads, through-window package delivery inevitable

pdf icon news icon

"Drones learn tricks with suspended loads, through-window package delivery inevitable". IEEE Spectrum. Oct. 13, 2014.

Penn’s GRASP Lab receives $5.5 million for ‘fast, light and autonomous’ flying robots

pdf icon news icon

"Penn’s GRASP Lab receives $5.5 million for ‘fast, light and autonomous’ flying robots". Penn News. Nov. 3, 2015.

Survey of advancements towards fast flight on quadrotors

pdf icon bibtex icon

“Autonomous flying”, in: Annual Reviews in Control, Robotics, and Autonomous Systems, 1st ed, vol. 1. N. Leonard, Ed. California: Annual Reviews, 2018. In press.

Survey of networked robotics

pdf icon bibtex icon

“Networked robotics”, in: Encyclopedia of Robotics, 1st ed, M. H. Ang, Ed. Berlin Heidelberg: Springer-Verlag, 2019. In press.

Aerial Robotics Coursera course

Our Coursera course on Aerial Robotics, part of a 5-course series on Robotics from the Penn GRASP Lab, is now live!

coursera aerial robotics

GRASP Research Experience for Undergraduates

During the summer of 2014, I worked with an incredible REU student on a research project titled “Autonomous Flight and Landing of a Quadrotor on a Moving Ground Vehicle Using April Tag Vision-Based Control”, which won the Best Presentation Award!

Autonomous Flight and Landing of a Quadrotor by Sarah Cen

Army Educational Outreach Program, Research & Engineering Apprenticeship Program

During the summer of 2017, I served as a mentor for the Army Educational Outreach Program, Research & Engineering Apprenticeship Program. I worked with a high school student to quantify and analyze inter-robot aerodynamic effects in a team of bitcraze Crazyflie robots during formation flight.

team of crazyflie quadrotors

USA Science and Engineering Festival

In April 2016, the GRASP lab hosted a booth at the USA Science and Engineering Festival, featuring our running, hopping, rolling, and flying robots!

GRASP lab robot

FIRST Lego League

From 2014–2016, I was honored to volunteer as a judge for the FIRST Lego League Championships at Penn!

lego robot

Rube Goldberg Celebration

From 2011–2012, I served as the Science Programming Assistant for the Cotsen Children’s Library at Princeton University, where I helped organize and facilitate events at the intersection of science and literature. One of our most successful initiatives was the first Rube Goldberg celebration, which featured this book-turning Rube Goldberg Machine (constructed with Tanner DeVoe).

Princeton Engineering Education for Kids (PEEK)

I was involved in Princeton Engineering Education for Kids (PEEK) from 2009–2013 as a volunteer and 2010–2012 as co-project coordinator. Our flagship initiative was a five-lesson series with every third grade classroom in the Princeton Regional School District. We also worked with a middle school engineering club, the Princeton Public Library, and presented at one-time outreach events throughout the school year.

Robot made by kids

Profile photo of Sarah Y Tang


Nuro. Robotics Software Engineer.


Google Self-Driving Car @ X. Software Engineering Intern. Summer 2016.

Vijay Kumar Lab @ UPenn GRASP Lab. PhD Researcher. 2013–2018.

Astrobotic @ Carnegie Mellon University Robotics Institute. Research Scholar. Summer 2012.

Lab for Autonomous and Intelligent Robots @ Princeton University (lab permanently at Harvey Mudd College). Undergraduate Researcher. 2011–2012.


PhD. University of Pennsylvania, Mechanical Engineering & Applied Mechanics. Dec. 2018.

MSE. University of Pennsylvania, Robotics. Dec. 2015.

BSE. Princeton University, Mechanical & Aerospace Engineering. June 2013.

pdf icon

CURRICULUM VITAE. Updated Oct., 2019.