Akarsh Kumar

Contact

akarshkumar0101(at)gmail(dot)com

I'm a Ph.D. student at MIT CSAIL working with Phillip Isola.
I'm also a research intern at Sakana AI working with Yujin Tang and David Ha.
My research is supported by the NSF GRFP!

My research interests include:

Applying principles from natural evolution and artificial life to create better AI systems.
Open-ended processes which keep creating interesting artifacts indefinitely.
Emergent intelligence from scratch, without the internet, like how natural evolution created us.

In practice, I'm espcially interested in meta-learning, RL, automatic environment generation, and multi-agent self-play.

If you're interested in working with me, please reach out!

Research

Automating the Search for Artificial Life with Foundation Models

Akarsh Kumar, Chris Lu, Louis Kirsch, Yujin Tang, Kenneth O. Stanley, Phillip Isola, David Ha

arXiv 2024

TLDR: A new ALife paradigm using CLIP to search for (1) target, (2) open-ended, and (3) diverse simulations.
Helps to understand "life as it could be" in arbitrary substrates.

[Paper (web)] [Paper (pdf)] [Blog] [Code] [Tweet]

Learning In-Context Decision Making with Synthetic MDPs

Akarsh Kumar, Chris Lu, Louis Kirsch, Phillip Isola

AutoRL @ ICML 2024

TLDR: In-context RL agents trained on only synthetic MDPs generalize to real MDPs.

[Paper]

Effective Mutation Rate Adaptation through Group Elite Selection

Akarsh Kumar, Bo Liu, Risto Miikkulainen, Peter Stone

GECCO 2022

TLDR: Why genetic algorithms fail to self-adapt their mutation rate and how group selection fixes it.

[Paper] [Code]

Physically Plausible Pose Refinement using Fully Differentiable Forces

Akarsh Kumar, Aditya R. Vaidya, Alexander G. Huth

EPIC @ CVPR 2021

TLDR: Accurately refining pose estimations by differentiably modeling the physics of the scene.

[Paper] [Presentation]