Filipe Veiga

Quick Info

Research Interests

Machine Learning, Robotics, Tactile Exploration, Control

More Information

Curriculum Vitae Publications Google Citations

Contact Information

Mail. Filipe Veiga,
TU Darmstadt, FG IAS,
Hochschulstr. 10, 64289 Darmstadt
Office. Room E323, Building S2|02

Filipe Veiga joined the Intelligent Autonomous System (IAS) lab of Technische Universitaet Darmstadt (roughly tranlated as Darmstadt University of Technology) on September 1st, 2013 as a PhD student. During his PhD his work on Machine Learning for Robot Grasping, Manipulation and tactile Exploration under the supervision of Jan Peters focuses on improving the dextrous capability of robots and is part of the TACMAN Project.

Before that, he got his Master Degree in Electrical and Computer Engineering at Instituto Superior Técnico (translates roughly into Higher Technical Institute) in Lisbon, Portugal. There he completed his Master Thesis on Robotic Grasp Optimization from Contact Force Analysis under the supervision of Alexandre Bernardino and José Santos-Victor.

Robotic Grasping and Manipulation has been quite a popular topic in the Robotics research community. While Grasping is now possible for robots with simple gripper end-effectors, robots equipped with dexterous manipulators still struggle to perform grasping tasks. Dexterity also adds the possibility of manipulating the grasped objects within the hand. Having robots grasping and manipulating objects in order to execute tasks or help a human with his task would allow robots into all sorts of new interesting settings. Using Machine Learning and exploring Tactile feedback, Filipe expects to tackle some of these challenging topics during his PhD.

Research Interests

Robotics, Control, Machine Learning, Artificial Intelligence, Robot Grasping, Robot Manipulation, Tactile Exploration, Event Based Control, Learning from Unstructured Data, Feature Learning, Pattern Recognition.

Key References

  1. Veiga, F.F.; Peters, J. (2016). Can Modular Finger Control for In-Hand Object Stabilization be accomplished by Independent Tactile Feedback Control Laws?, arXiv.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  2. Veiga, F.F.; van Hoof, H.; Peters, J.; Hermans, T. (2015). Stabilizing Novel Objects by Learning to Predict Tactile Slip, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  3. Veiga, F.; Bernardino, A. (2013). Active tactile exploration for grasping, Proceedings of the ICRA 2013 Workshop on Autonomous Learning.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  4. Veiga, F.; Bernardino, A. (2012). Towards Bayesian Grasp Optimization with Wrench Space Analysis, Proceedings of the IROS 2012 Workshop Beyond Robot Grasping.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]


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