Ayush Agarwal

I am an incoming MSCS student at Stanford University. Previously, I was a student at the Georgia Institute of Technology, majoring in Computer Science.. My interests are in robotics, computer vision, and reinforcement learning. Currently, I am advised by Prof. Animesh Garg.

As an active member of the People, AI, and Robots (PAIR) Lab, I am developing novel techniques to scale robot learning through both data-driven and algorithmic approaches. In the past, I have interned at Johnson & Johnson and LinkedIn as a Software Engineering Intern, tackling open-ended problems across diverse domains.

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Research

I'm interested in developing learning algorithms that can effectively leverage diverse, multimodal data sources to enable robust robot learning. While robotics has been constrained by limited data compared to other AI domains, I believe the key lies not just in collecting more data, but in creating algorithms that can optimally extract knowledge from heterogeneous sources, including simulation, real-robot demonstrations, and internet videos.

Some questions I am interested in discussing and exploring:

• How can we design learning algorithms that effectively transfer knowledge across diverse domains and modalities?
• How can we develop algorithms that bridge the sim-to-real gap for dynamic sensorimotor control?
• How can we enable robots to learn causal reasoning and abstract concepts from data to enable better generalization?
• What inductive biases and architectural choices enable efficient learning from sub-optimal demonstrations?

COBALT: Crowdsourcing Robot Learning via Cloud-Based Teleoperation with Smartphones

A teleoperation platform that enables uninterrupted, concurrent data collection from multiple users worldwide using everyday devices like smartphones. This work not only significantly reduces teleoperation costs but also demonstrates the scalability of smartphone-based robot learning by successfully crowdsourcing 7500+ high-quality robot demonstrations from 50+ inexperienced teleoperators across nine countries.

Implementing Transformer Architectures for Audio Source Separation

A novel transformer-based architecture that replaces BiLSTM blocks in the Open-Unmix model, achieving superior training efficiency and reduced inter-source interference on MUSDB18-HQ.

Figure adapted from Manilow, E., Seetharman, P., & Salamon, J. “Open Source Tools & Data for Music Source Separation” (2020)

Design and source code from Jon Barron's website