Jens Lundell

Postdoctoral researcher in Robotics @ KTH. PhD in Robotics from Aalto University.

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I am Jens Lundell, a Postdoctoral researcher working with Danica Kragic in the Robotics, Perception and Learning Lab (RPL) at KTH in Stockholm, Sweden. My research interests is data-driven methods for robotic manipulation and grasping. I am currently working on in-hand manipulation of rigid objects, also known as intrinsic dexterity, deformable object manipulation, and constrained robotic grasping.

I did my Ph.D. in the Intelligent Robotics group at Aalto University (Finland) with Prof. Ville Kyrki and Ph.D. Francesco Verdoja. My Ph.D. thesis addressed the problem of probabilistic 6-degree-of-freedom multi-finger grasping of objects in clutter. My solution to those problems was to explicitly shape-complete each object in the scene using deep learning, and plan grasps on those reconstructions. I also focused on using physics simulators to gather synthetic data for training deep networks.

I hold a Master’s degree in Space Science and Technology and a Bachelor’s degree in Automation and Systems Technology, both from Aalto University.

My main research interests:

  • Robotic Grasping
  • Robotic Manipulation
  • Deep Learning

selected publications

2025

  1. SeqGrasp.png
    Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generation
    Haofei Lu, Yifei Dong, Zehang Weng, and 2 more authors
    arXiv preprint arXiv:2503.22370, 2025

2024

  1. ClothSplatting.gif
    Cloth-Splatting: 3D State Estimation from RGB Supervision for Deformable Objects
    Alberta Longhini, Marcel Büsching, Bardienus Pieter Duisterhof, and 4 more authors
    In 8th Annual Conference on Robot Learning, 2024
  2. DexDiffuser.png
    Dexdiffuser: Generating dexterous grasps with diffusion models
    Zehang Weng, Haofei Lu, Danica Kragic, and 1 more author
    IEEE Robotics and Automation Letters, 2024

2021

  1. DDGC.png
    DDGC: Generative deep dexterous grasping in clutter
    Jens Lundell, Francesco Verdoja, and Ville Kyrki
    IEEE Robotics and Automation Letters, 2021