Vaibhav Saxena
Vaibhav Saxena
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I am a ML Ph.D. student at Georgia Tech, where I work at the Robot Learning and Reasoning Lab (RL2) directed by Prof. Danfei Xu . My research involves generative models for computer vision ποΈ, memory π§ , and decision-making π¦Ύ. These days, I am focused on building generalizable robot policies using minimal expert demonstrations!
Β In 2019, I graduated with a Master of Science in Applied Computing from the Department of Computer Science, University of Toronto, where I was advised by Prof. Jimmy Ba and Prof. Frank Rudzicz, and worked closely with Danijar Hafner. My research involved learning state-space models for long-horizon planning, deep reinforcement learning, and multi-modal representation learning.
See more about my professional experience here and also on my CV (Aug, 2025).
News
News
[Jan 2025] MimicLabs accepted at ICLR 2025!
[Aug 2024] Our paper on "C3DM: Constrained-Context Conditional Diffusion Models for Imitation Learning" got accepted at TMLR 2024
[Mar 2024] We will be organizing the βData Generation for Robotics Workshop" at RSS 2024 in Delft, Netherlands!
[Feb 2023] I will be returning to Autodesk Research as a Research Intern during Summer 2023
[Jan 2023] Our paper on "Generalizable Pose Estimation using Implicit Scene Representations" got accepted at ICRA 2023
[Dec 2022] I will be presenting our paper on "Guiding Exploration Towards Impactful Actions" at the Deep RL Workshop in NeurIPS 2022
[Feb 2022] I will be spending the Summer of 2022 as a Research Intern at Autodesk Research
[Dec 2021] I will be presenting our paper on "Clockwork Variational Autoencoders" at NeurIPS 2021 [tweet]
[Aug 2021] Joining the ML Ph.D. program at Georgia Tech!
[Apr 2021] Clockwork VAEs featured on Towards Data Science!
ResearchΒ (representative works highlighted)
ResearchΒ (representative works highlighted)
What Matters in Learning from Large-Scale Datasets for Robot Manipulation
What Matters in Learning from Large-Scale Datasets for Robot Manipulation
Vaibhav Saxena, Matthew Bronars, Nadun Ranawaka, Kuancheng Wang, Woo Chul Shin, Soroush Nasiriany, Ajay Mandlekar, Danfei Xu
ICLR 2025
π Paper
π Project Website
π₯οΈ Code
π Tweet
C3DM: Constrained-Context Conditional Diffusion Models for Imitation Learning
C3DM: Constrained-Context Conditional Diffusion Models for Imitation Learning
π Paper
π Project Webpage
π Tweet
Query image
Pose estimation using i-ΟSRN
Generalizable Pose Estimation using Implicit Scene Representations
Generalizable Pose Estimation using Implicit Scene Representations
Vaibhav Saxena, Kamal Rahimi Malekshan, Linh Tran, Yotto Koga
ICRA, 2023
π Paper
π Project Website
π₯οΈ Code
π Blog
Guiding Exploration Towards Impactful Actions
Guiding Exploration Towards Impactful Actions
Vaibhav Saxena, Jimmy Ba, Danijar Hafner
Deep Reinforcement Learning Workshop
NeurIPS, 2022
Dyna-AIL : Adversarial Imitation Learning by Planning
Dyna-AIL : Adversarial Imitation Learning by Planning
Vaibhav Saxena, Srinivasan Sivanandan, Pulkit Mathur
Beyond "Tabula Rasa" in Reinforcement Learning Workshop
ICLR, 2020
π Paper
Service
Service
Reviewer for
ML Conferences: NeurIPS, ICML, ICLR
Robotics Conferences: CoRL, RSS, ICRA
Journals: RA-L, TMLR
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