Priya Sundaresan

I am a 4th year PhD student at Stanford University, co-advised by Professors Dorsa Sadigh and Jeannette Bohg. I am interested in imitation learning and representation learning for real-world manipulation. I am funded by the National Science Foundation Graduate Fellowship.

Before Stanford, I completed my B.S. and M.S. at UC Berkeley, where I worked with Professor Ken Goldberg in the AUTOLAB. I have also previously spent summers interning at Amazon Robotics and [Google] Intrinsic.

Please feel free to reach out!

priyasun@stanford.edu / CV / Github / Google Scholar / X

Research

	What's the Move? Hybrid Imitation Learning via Salient Points Priya Sundaresan, Hengyuan Hu, Quan Vuong, Jeannette Bohg, Dorsa Sadigh To Appear, International Conference on Learning Representations (ICLR), 2025. Website / PDF
	Motion Tracks: A Unified Representation for Human-Robot Transfer in Few-Shot Imitation Learning Juntao Ren, Priya Sundaresan, Dorsa Sadigh, Sanjiban Choudhury, Jeannette Bohg To Appear, International Conference on Robotic Automation (ICRA), 2025. Website / PDF
	FLAIR: Feeding via Long-horizon AcquIsition of Realistic dishes Rajat Kumar Jenamani, Priya Sundaresan, Maram Sakr, Tapomayukh Bhattacharjee†, Dorsa Sadigh† Robotic Science and Systems (RSS), 2024. Website / PDF / SF Chronicle
	RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches Priya Sundaresan, Quan Vuong, Jiayuan Gu, Peng Xu, Ted Xiao, Sean Kirmani, Tianhe Yu, Michael Stark, Ajinkya Jain, Karol Hausman, Dorsa Sadigh, Jeannette Bohg, Stefan Schaal* Conference on Robot Learning (CoRL), 2024. ★Oral Presentation★ Website / PDF / VentureBeat
	RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches Jiayuan Gu, Sean Kirmani, Paul Wohlhart, Yao Lu, Montserrat Gonzalez Arenas, Kanishka Rao, Wenhao Yu, Chuyuan Fu, Keerthana Gopalakrishnan, Zhuo Xu, Priya Sundaresan, Peng Xu, Hao Su, Karol Hausman, Chelsea Finn, Quan Vuong, Ted Xiao International Conference on Learning Representations (ICLR), 2024. ★Spotlight (5%)★ Website / PDF
	Open X-Embodiment: Robotic Learning Datasets and RT-X Models Open X-Embodiment Collaboration [>150 authors] International Conference on Robotic Automation (ICRA), 2024. ★Best Paper Award★ Website / PDF
	KITE: Keypoint-Conditioned Policies for Semantic Manipulation Priya Sundaresan, Suneel Belkhale, Dorsa Sadigh, Jeannette Bohg Conference on Robot Learning (CoRL), 2023. Website / PDF
	Learning Sequential Acquisition Policies for Robot-Assisted Feeding Priya Sundaresan, Jiajun Wu, Dorsa Sadigh Conference on Robot Learning (CoRL), 2023. Website / PDF
	In-Mouth Robotic Bite Transfer with Visual and Haptic Sensing Lorenzo Shaikewitz, Yilin Wu, Suneel Belkhale, Jennifer Grannen, Priya Sundaresan, Dorsa Sadigh International Conference on Robotics and Automation (ICRA), 2023.* Website / PDF
	Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding Priya Sundaresan, Suneel Belkhale, Dorsa Sadigh Conference on Robot Learning (CoRL), 2022. ★Oral Presentation★ Website / PDF / Stanford HAI Blogpost
	DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects Priya Sundaresan, Rika Antonova, Jeannette Bohg International Conference on Intelligent Robots and Systems (IROS), 2022. Website / PDF
	A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation Rika Antonova, Jingyun Yang, Priya Sundaresan, Dieter Fox, Fabio Ramos, Jeannette Bohg IEEE Robotics and Automation Letters (RA-L), 2022. PDF
	Disentangling Dense Multi-Cable Knots Vainavi Viswanath, Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan, Ashwin Balakrishna, Ellen Novoseller, Jeffrey Ichnowski, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg International Conference on Intelligent Robots and Systems (IROS), 2021.* Website / PDF
	Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan, Ashwin Balakrishna, Jeffrey Ichnowski, Ellen Novoseller, Minho Hwang, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg Robotics: Science and Systems (RSS), 2021. Website / PDF
	Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan, Ashwin Balakrishna, Daniel Seita, Jennifer Grannen, Minho Hwang, Ryan Hoque, Joseph E. Gonzalez, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg International Conference on Robotics and Automation (ICRA), 2021. Website / PDF
	MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects. Priya Sundaresan, Aditya Ganapathi, Brijen Thananjeyan, Ashwin Balakrishna, Daniel Seita, Ryan Hoque, Joseph Gonzalez, Ken Goldberg International Conference on Intelligent Robots and Systems (IROS), Workshop on Robotic Manipulation of Deformable Objects, 2020. PDF
	Untangling Dense Knots by Learning Task-Relevant Keypoints Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan, Jeffrey Ichnowski, Ashwin Balakrishna, Minho Hwang, Vainavi Viswanath, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg Conference on Robot Learning (CoRL), 2020. ★Oral Presentation★ Website / PDF
	Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data. Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan, Ashwin Balakrishna, Michael Laskey, Kevin Stone, Joseph E. Gonzalez, Ken Goldberg. International Conference on Robotics and Automation (ICRA), 2020. Website / PDF
	Automated Extraction of Surgical Needles from Tissue Phantoms Priya Sundaresan, Brijen Thananjeyan, Johnathan Chiu, Danyal Fer, Ken Goldberg Conference on Automation Science and Engineering (CASE), 2019. PDF

For Fun

Doge Dodge
This is a silly little game I made which is heavily inspired by the Chrome T-Rex game that pops up when there's bad connectivity (s/o to UC Berkeley's wifi!), as well as my favorite game of all time, Crossy Road!!
Credits: tile art and sound effects, doge art, cactus art, background art, rock art, spike art.

Shoutout to Jon Barron for the awesome website template <3.