Omkar Patil

Hi there! I’m a PhD student @ ASU working in the Logos Robotics Lab under the guidance of Dr. Nakul Gopalan. My main focus area is compositional robot learning, where I try to develop methods that utilize the principle of compositionality to learn skills efficiently. Prior to joining ASU, I worked for 3 years at Wells Fargo where I did a variety of research in NLP. In my previous life, I did my undergraduate and master’s from IIT Madras (India), focusing on Robotics. My master’s thesis was done under the guidance of Dr. Anurag Mittal.

Apart from my work, I love hiking and have done several extensive hikes in the Himalayas and plan to do some more in the US! I have a strong liking for landscapes and nature.

news

May 01, 2025	Learning Factorized Diffusion Policies for Conditional Action Diffusion (webpage) has been accepted for ICRA 2025 Workshop on Foundation Models and Neuro-Symbolic AI for Robotics.
Oct 21, 2024	Few-shot Compositional Learning of Motions (webpage) has been accepted for Compositional Learning Workshop @ NeurIPS 2024.
Oct 01, 2024	Two co-authored papers Learning Temporally Composable Task Segmentations with Language and Hardware-Software Co-Design for Path Planning by Drones have been accepted for IROS 2024! I will also presenting these works in Abu Dhabi.
Aug 01, 2023	Joined the PhD program at ASU!

latest posts

May 10, 2024	Explaining Life from the Lens of Reinforcement Learning
Mar 31, 2020	COVID Contact Tracing Explained — Corona Kavach

selected publications

Learning Factorized Diffusion Policies for Conditional Action Diffusion

Omkar Patil , Eric Rosen, and Nakul Gopalan

In ICRA 2025 Workshop on Foundation Models and Neuro-Symbolic AI for Robotics, 2025

Abs Bib HTML

Diffusion models have emerged as a promising choice for learning robot skills from demonstrations. However, they face three problems: diffusion models are not sample-efficient, data is expensive to collect in robotics, and the space of tasks is combinatorially large. The established way of learning diffusion policies has little room to accommodate solutions for the aforementioned challenges, in addition to scaling the model size and paired observation-action data. In this work, we propose a novel method for training diffusion models termed ‘Composable Diffusion Guidance’ CoDiG to compositionally learn diffusion policies for robot skills with respect to different observation modalities, such as proprioception, vision and tactile. CoDiG provides more flexibility to deal with such observational modalities, leading to sample-efficiency gains over 20% in applicable tasks. CoDiG opens up more avenues for research on foundation models as it ameliorates the requirement of scaling all the observational modalities together during data collection.
@inproceedings{patil2025learning, title = {Learning Factorized Diffusion Policies for Conditional Action Diffusion}, author = {Patil, Omkar and Rosen, Eric and Gopalan, Nakul}, booktitle = {ICRA 2025 Workshop on Foundation Models and Neuro-Symbolic AI for Robotics}, year = {2025}, }