Priya Sundaresan

About

Priya Sundaresan is a robotics researcher whose work spans deformable object manipulation, robot-assisted feeding, and imitation learning — areas where perception, planning, and physical interaction converge in some of robotics' most demanding open problems. She is perhaps best known for her pioneering use of dense object descriptors trained on synthetic data to enable robots to manipulate flexible materials in the real world. Her 2020 paper on rope manipulation using synthetic depth data has accumulated over 100 citations, reflecting its significant influence on sim-to-real transfer for deformable objects, while her complementary fabric manipulation work has garnered an additional 55 citations. Sundaresan extended these contributions to tackle particularly complex scenarios, including untangling dense knots and disentangling multi-cable systems — challenges requiring both geometric reasoning and recovery from failure. More recently, she has broadened her research portfolio into robot-assisted feeding, developing visuo-haptic strategies for food skewering and safe in-mouth bite transfer, demonstrating a commitment to socially impactful robotics. Her 2025 work on Motion Tracks further highlights her interest in scalable imitation learning from human video demonstrations. Across her career, Sundaresan has consistently bridged the gap between simulation-based learning and robust real-world robot performance.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data

   103 citations · 2020
2. 2
   Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics

   55 citations · 2021
3. 3
   Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies

   26 citations · 2021
4. 4
   Disentangling Dense Multi-Cable Knots

   19 citations · 2021
5. 5
   Learning to Smooth and Fold Real Fabric Using Dense Object Descriptors Trained on Synthetic Color Images

   19 citations · 2020
6. 6
   Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data

   16 citations · 2020
7. 7
   Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics

   16 citations · 2020
8. 8
   In-Mouth Robotic Bite Transfer with Visual and Haptic Sensing

   13 citations · 2023
9. 9
   Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding

   7 citations · 2022
10. 10
    Motion Tracks: A Unified Representation for Human-Robot Transfer in Few-Shot Imitation Learning

    6 citations · 2025

Key Collaborators

Contact & Links