Lars Ankile

I'm a visiting researcher at the Improbable AI Lab at MIT CSAIL working on robot learning with Prof. Pulkit Agrawal.


Formal Bio     Github     G. Scholar     LinkedIn     Twitter     Resume

lars.ankile@gmail.com / ankile@mit.edu

Some recent highlights from our research:
Robot Learning with Super-Linear Scaling

Robot Learning with Super-Linear Scaling

Marcel Torne, Arhan Jain, Jiayi Yuan, Vidaaranya Macha, Lars L Ankile, Anthony Simeonov, Pulkit Agrawal, Abhishek Gupta
Under review
Webpage  •   PDF

DexHub and DART

DexHub and DART: Towards Internet Scale Robot Data Collection

Younghyo Park, Jagdeep Singh Bhatia, Lars L Ankile, Pulkit Agrawal
ICRA'25
Webpage  •   PDF

From Imitation to Refinement

From Imitation to Refinement--Residual RL for Precise Visual Assembly

Lars L Ankile, Anthony Simeonov, Idan Shenfeld, Marcel Torne, Pulkit Agrawal
ICRA'25
Webpage  •   PDF  •   Code

Diffusion Policy Policy Optimization

Diffusion Policy Policy Optimization

Allen Z Ren, Justin Lidard, Lars L Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, Max Simchowitz
ICLR'25
Webpage  •   PDF  •   Code

JUICER

JUICER: Data-Efficient Imitation Learning for Robotic Assembly

Lars L Ankile, Anthony Simeonov, Idan Shenfeld, Pulkit Agrawal
IROS'24
Webpage  •   PDF  •   Code

AMBER

AMBER: An Entropy Maximizing Environment Design Algorithm for Inverse Reinforcement Learning

Paul Nitschke, Lars L Ankile, Eura Nofshin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan
ICML'24 Workshop on Models of Human Feedback for AI Alignment
PDF

Adversarial Behavior

I See You! Robust Measurement of Adversarial Behavior

Lars L Ankile, Matheus XV Ferreira, David Parkes
TLDR'24 @ Columbia University; Multi-Agent Security Workshop @ NeurIPS'23
PDF  •   Code

Discovering User Types

Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning

Lars L Ankile, Brian Ham, Kevin Mao, Eura Shin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan
First Workshop on Theory of Mind in Communicating Agents @ ICML'23
PDF  •   Code

Denoising Diffusion Models

Denoising Diffusion Probabilistic Models as a Defense against Adversarial Attacks

Lars L Ankile, Anna Midgley, Sebastian Weisshaar
arXiv preprint, 2023
PDF

Master's Thesis

Exploration of Forecasting Paradigms and a Generalized Forecasting Framework

Lars L Ankile, Kjartan Krange
Master's thesis, NTNU, 2022
PDF  •   Code

Deep Learning and Linear Programming

Deep Learning and Linear Programming for Automated Ensemble Forecasting and Interpretation

Lars L Ankile, Kjartan Krange
arXiv preprint, 2022
PDF  •   Code  •   Slides

Approximate Strategy-Proofness

Approximate Strategy-Proofness in Large, Two-Sided Matching Markets

Lars L Ankile, Kjartan Krange, Yuto Yagi
arXiv preprint, 2019
PDF  •   Code

Deep CNN Survey

Deep Convolutional Neural Networks: A survey of the foundations, selected improvements, and some current applications

Lars L Ankile, Morgan Feet Heggland, Kjartan Krange
arXiv preprint, 2020
PDF

2024

Learning Fine and Dexterous Manipulation Workshop @ CoRL - Spotlight presentation on "From Imitation to Refinement"

2024

Mastering Robotic Manipulation Workshop @ CoRL - Spotlight presentation on "Diffusion Policy Policy Optimization"

2024

IROS Learning Track Spotlight - "JUICER: Data-Efficient Imitation Learning for Robotic Assembly"

2023

Multi-Agent Security Workshop @ NeurIPS - "I See You!" oral presentation

2023

Harvard MS Data Science Orientation Research Panel - Student Research Opportunities

2022

NTNU PhD Course on Economic and Financial Forecasting - "Ensemble forecasting and the M4 competition"

2025

Conference Reviewer - RSS'25

2024

High School Student Research Mentor - Advising projects on computer vision and robotics

2023

Conference Reviewer - CoRL, ICML Workshops