Pablo Palafox

Pablo Palafox

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Co-Founder & CEO
San Francisco, California, United States

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Jobs verified_user 0% verified
  • HappyRobot
    Co-Founder & CEO
    HappyRobot
    Nov 2023 - Current (2 years 8 months)
    Transforming enterprises with AI workers
  • H
    Co-Founder
    Happyrobot YC S
    Nov 2023 - Current (2 years 8 months)
    Building AI assistants for logistics.
  • PRO Unlimited
    Research Intern
    PRO Unlimited
    Oct 2021 - Jan 2022 (4 months)
  • Meta
    Research Intern
    Meta
    May 2021 - Sep 2021 (5 months)
    Working at Facebook Reality Labs (FRL)
  • Technical University of Munich
    Researcher and PhD Candidate @ 3D AI Lab under Prof. Angela Dai
    Technical University of Munich
    Dec 2019 - Mar 2022 (2 years 4 months)
    Supervisor: Prof. Angela Dai Have a look at my projects -> https://pablopalafox.github.io
  • Technical University Munich
    Research Assistant @ Daniel Cremers' Computer Vision Group
    Technical University Munich
    Feb 2019 - Jan 2020 (1 year)
    Topic: Local Tracking and Mapping for Direct Visual SLAM Advisor: Nikolaus Demmel - Supervisor: Prof. Daniel Cremers
  • Technical University Munich
    Research Assistant @ Matthias Niessner's Visual Computing & Artificial Intelligence Group
    Technical University Munich
    Oct 2018 - May 2019 (8 months)
    Topic: Sceneflow prediction in Pointclouds and Voxelgrids using Deep Learning Advisor: Prof. Matthias Niessner
  • IPB Systems
    Software Engineer [Sidejob]
    IPB Systems
    Oct 2016 - Jul 2017 (10 months)
    Development of wifi- and sound-based anti-drone systems using Deep Learning.
  • Universidad Politécnica de Madrid
    Undergraduate Student Researcher [Research Grant]
    Universidad Politécnica de Madrid
    Nov 2015 - Jun 2016 (8 months)
    http://blogs.upm.es/robcib/2016/09/21/landing-on-ugv/ During this research grant, awarded by the Spanish Ministry of Education, I worked at the Centre for Automation and Robotics (UPM-CSIC) of the Technical University of Madrid developing an Autonomous Landing system for an Unmanned Aerial Vehicle. More precisely, we implemented an algorithm that allowed a drone to autonomously take-off from a moving platform, get to a certain height, detect and follow the moving platform autonomously and, when told to (by pressing a button in a controller), land on the platform while the latter was still in motion. This project idea was motivated by the Mohamed Bin Zayed International Robotics Challenge.
  • Robdos Team Underwater Robotics
    Computer Vision Engineer
    Robdos Team Underwater Robotics
    Sep 2014 - Sep 2017 (3 years 1 month)
    Robdos Team is a multidisciplinary student association from the Technical University of Madrid developing an Autonomous Underwater Vehicle. We participated in euRathlon 2015 and 2017. I was in charge of detecting buoys and mannequins underwater. I implemented both a C++ algorithm based on traditional Computer Vision for detecting the former and a trained cascade person-classifier for detecting the mannequins. We used ROS as a framework for our robot system.
Education verified_user 0% verified
  • P
    Professional Degree in Music, Piano
    Professional Music Conservatory Sebastián Durón
  • Universidad Politécnica de Madrid
    Master of Science - MS, Robotics and Electronics
    Universidad Politécnica de Madrid
    Double Degree with Technical University of Munich
  • thePower
    ThePowerMBA Future Leaders Program, Business Administration and Management, General
    thePower
  • Technical University of Munich
    Master's Thesis, Computer Vision Group
    Technical University of Munich
    Topic: Local Tracking and Mapping for Direct Visual SLAM Advisor: Nikolaus Demmel Supervisor: Prof. Daniel Cremers
  • Technical University of Munich
    Master of Science - MS, Mechanical Engineering
    Technical University of Munich
    Projects: - "Analyzing SLAM: RGB-Only & RGB-D". Project for the course "3D Scanning and Motion Capture" at Visual Computing Group (headed by Prof. Matthias Niessner) - "Development and Implementation of a Deep Learning-based Computer Vision Pipeline for Lane Detection" (Semester Project at Chair of Automotive Technology )
  • Universidad Politécnica de Madrid
    Bachelor of Science - BS, Robotics and Electronics
    Universidad Politécnica de Madrid
  • Technical University of Munich
    Doctor of Philosophy - PhD, Computer Vision & Deep Learning
    Technical University of Munich
Awards verified_user 0% verified
  • MUTUA MADRILEÑA
    Postgraduate Scholarship "Becas Postgrado Mutua Madrileña"
    MUTUA MADRILEÑA
    Jul 2017
    Postgraduate Scholarship awarded to 40 Spanish students for studying a Masters abroad.
  • J
    Jumping Talent
    Mar 2016
    Selected as one of the 96 most promising university students in Spain (out of more than 5.000 candidates) to take part in an event sponsored by 12 top multinational companies.
  • Comunidad de Madrid
    Excellence Scholarship
    Comunidad de Madrid
    Mar 2013
    Scholarship awarded for an excellent GPA
Publications verified_user 0% verified
  • S
    SemanticDepth: Fusing Semantic Segmentation and Monocular Depth Estimation for Enabling Autonomous Driving in Roads with
    Sensors MPDI
    Jul 2019
    Typically, lane departure warning systems rely on lane lines being present on the road. However, in many scenarios, e.g., secondary roads or some streets in cities, lane lines are either not present or not sufficiently well signaled. In this work, we present a vision-based method to locate a vehicle within the road when no lane lines are present using only RGB images as input. To this end, we propose to fuse together the outputs of a semantic segmentation and a monocular depth estimation architecture to reconstruct locally a semantic 3D point cloud of the viewed scene. We only retain points belonging to the road and, additionally, to any kind of fences or walls that might be present right at the sides of the road. We then compute the width
  • A
    Robust Visual-Aided Autonomous Takeoff, Tracking, and Landing of a Small UAV on a Moving Landing Platform for Life-Long
    Applied Sciences MDPI
    Jun 2019
    Robot cooperation is key in Search and Rescue (SaR) tasks. Frequently, these tasks take place in complex scenarios affected by different types of disasters, so an aerial viewpoint is useful for autonomous navigation or human tele-operation. In such cases, an Unmanned Aerial Vehicle (UAV) in cooperation with an Unmanned Ground Vehicle (UGV) can provide valuable insight into the area. To carry out its work successfully, such as multi-robot system requires the autonomous takeoff, tracking, and landing of the UAV on the moving UGV. Furthermore, it needs to be robust and capable of life-long operation. In this paper, we present an autonomous system that enables a UAV to take off autonomously from a moving landing platform, locate it using visual
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