Arthur Jakobsson

Arthur Jakobsson

Robotics Researcher @ CMU

Birds, photography, and computer science.

Hi! I'm a full-time masters student at Carnegie Mellon University working in the Momentum Lab with Professor Jeff Ichnowski. I'm applying to PhD programs in computer vision and robotics for Fall 2026, and I'm specifically excited about machine learning-enabled soft and articulated object manipulation, high-speed manipulation, and creative sensing.

My most recent work is Wiggle and Go, a system that enables a robot to swing ropes at 3D targets zero-shot: by observing a few seconds of rope motion, predicting physical parameters, and conditioning a goal-directed policy on them. In my undergrad, I was part of the SBPL Lab, advised by Professor Maxim Likhachev where I worked on machine learning for multi-agent path-finding.

From 2022 to 2025 I served as a TA, lead TA, and then course instructor, for 15-122 Principles of Imperative Computation. I built and ran the bootcamp series now used across the course — over 3,000 cumulative student attendances. Teaching matters to me and I hope to get to keep teaching as I continue my academic career!

Outside of research I love birdwatching, photography, biking, and badminton.

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Featured papers

Selected publications

Two highlights below — full list on /publications.

Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation

Arthur Jakobsson, Abhinav Mahajan, Karthik Pullalarevu, Krishna Suresh, Yunchao Yao, Yuemin Mao, Bardienus Duisterhof, Shahram Najam Syed, Jeffrey Ichnowski

arXiv preprint · 2026

We present a framework that lets robots perform dynamic rope manipulation tasks zero-shot — without large real-world datasets or iterative trial-and-error. A system identification module observes a few seconds of rope motion to predict descriptive physical parameters, which then condition goal-directed action prediction. On a 3D target-striking task we achieve 3.55 cm accuracy compared to 15.34 cm for the no-sysID baseline.

Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large-Scale Imitation Learning for MAPF

Arthur Jakobsson*, Rishi Veerapaneni*, Kevin Ren, Samuel Kim, Jiaoyang Li, Maxim Likhachev
* equal contribution

IEEE International Conference on Robotics and Automation (ICRA) · 2025

We show that combining a small imitation-learning policy with the CS-PIBT collision-shielding rule outperforms large-scale imitation learning for multi-agent path-finding (MAPF). Strong evidence that careful priors beat raw data scale for combinatorial planning problems.

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