Tucker Hermans

About

Tucker Hermans is a robotics researcher whose work spans tactile sensing, dexterous manipulation, robot learning, and human-robot interaction. He is perhaps best known for his foundational contributions to tactile-based robotic manipulation, particularly his highly cited work on learning in-hand manipulation with tactile features (165 citations) and predicting tactile slip to stabilize novel objects (105 citations), research that has significantly advanced how robots handle unknown objects without relying on pre-built models. His 2018 follow-up on grip stabilization (93 citations) further cemented this line of inquiry, demonstrating robust grasping in real-world, unstructured environments. Beyond manipulation, Hermans has made notable contributions to active tactile object exploration using Gaussian processes (96 citations), enabling robots to efficiently build object shape models through intelligent touch strategies. His 2013 work on visual saliency incorporating depth information (122 citations) reflects an early interest in rich perceptual representations. More recently, he has explored natural language correction of robot plans (67 citations), pointing toward more intuitive human-robot collaboration. With a diverse and highly cited portfolio totaling nearly 1,000 citations across core robotics challenges, Hermans stands as a significant contributor to the fields of robot perception, manipulation, and learning.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning robot in-hand manipulation with tactile features

   165 citations · 2015
2. 2
   An In Depth View of Saliency

   122 citations · 2013
3. 3
   Near-Optimal Area-Coverage Path Planning of Energy-Constrained Aerial Robots With Application in Autonomous Environmental Monitoring

   112 citations · 2020
4. 4
   Stabilizing novel objects by learning to predict tactile slip

   105 citations · 2015
5. 5
   Active tactile object exploration with Gaussian processes

   96 citations · 2016
6. 6
   Grip Stabilization of Novel Objects Using Slip Prediction

   93 citations · 2018
7. 7
   3D-Printing and Machine Learning Control of Soft Ionic Polymer-Metal Composite Actuators

   92 citations · 2019
8. 8
   Correcting Robot Plans with Natural Language Feedback

   67 citations · 2022
9. 9
   Evaluation of tactile feature extraction for interactive object recognition

   66 citations · 2015
10. 10
    Planning Multi-fingered Grasps as Probabilistic Inference in a Learned Deep Network

    64 citations · 2019

Key Collaborators

Contact & Links