Filipe Veiga

About

Filipe Veiga is a leading researcher in robotic tactile sensing and dexterous manipulation, whose work bridges the gap between physical interaction and intelligent control. His primary research areas include tactile perception, grip stabilization, and reinforcement learning for in-hand manipulation. Veiga’s major contributions center on understanding how robots can use touch to interact with unknown objects—from predicting slip to actively exploring object shapes. His highly cited 2020 review on tactile information (312 citations) provides a foundational framework for the field, while his work on SwingBot (109 citations) demonstrates how tactile exploration can enable dynamic manipulation tasks like swing-up movements. Veiga pioneered slip prediction for grip stabilization (105 citations), allowing robots to maintain stable grasps on novel objects without prior models. His innovative active tactile exploration using Gaussian processes (96 citations) improved sample efficiency in object shape modeling. Notably, Veiga’s hierarchical control decomposition and independent finger feedback strategies have advanced dexterous manipulation, while his application of reinforcement learning to page-flipping movements showcases tactile feedback in deformable object manipulation. With over 800 total citations across his top papers, Veiga’s research continues to shape how robots perceive and act through touch, making him a key figure in modern tactile robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   A Review of Tactile Information: Perception and Action Through Touch

   312 citations · 2020
2. 2
   SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulation

   109 citations · 2020
3. 3
   Stabilizing novel objects by learning to predict tactile slip

   105 citations · 2015
4. 4
   Active tactile object exploration with Gaussian processes

   96 citations · 2016
5. 5
   Grip Stabilization of Novel Objects Using Slip Prediction

   93 citations · 2018
6. 6
   Grip Stabilization through Independent Finger Tactile Feedback Control

   45 citations · 2020
7. 7
   Regularizing Reinforcement Learning with State Abstraction

   21 citations · 2018
8. 8
   Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks

   17 citations · 2020
9. 9
   Learning attribute grammars for movement primitive sequencing

   14 citations · 2019
10. 10
    Autonomous Learning of Page Flipping Movements via Tactile Feedback

    13 citations · 2022

Key Collaborators

Contact & Links