Hey! I'm Jeremy, and I'm a founding member of Dream Labs. I'm also a Machine Learning PhD student at Georgia Tech advised by Animesh Garg.
My research focuses on scaling robot learning by harnessing diverse sources of data. Specifically, I'm leveraging internet-scale human videos and building accessible platforms for robotics at scale, with the goal of enabling robots to generalize to new tasks and environments and operate reliably in the real world.
The Dream Labs Team
Blog Post |
Playground |
API
Dream-1 is a World Action Model that controls robots it has never seen, performs tasks it was never shown, and runs from $3/hour. Connect a new bimanual robot with parallel grippers to the API and it performs tasks out of the box, with no fine-tuning and no demonstrations from that robot.
Jeremy A. Collins*, Namra Patel*, Ayush Agarwal*, Shitij Govil*, Animesh Garg
In Submission
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Website
Given an image and text prompt, FloMo jointly predicts dense 3D motion (scene flow) and robot actions using a pretrained 5B-parameter video generation model using flow-matching. To tokenize scene flow, we transform instantaneous 3D velocities into RGB videos that are encoded by the pretrained video backbone's video encoder.
Jeremy A. Collins, Seungjae Lee, Rachanon Wachakorn, Krishnan Srinivasan, Animesh Garg,
Vitor Campagnolo Guizilini, Paarth Shah
In Submission
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Website
TAPESTRY treats robot actions as single patch tokens in the video model's latent space. Video latents and action patches share the same patch embedding layers and the same diffusion backbone. At test time, the model generates an action chunk in a single DDIM step from pure noise in 84ms on consumer hardware, no video generation required.
Jeremy A Collins*, Loránd Cheng*, Kunal Aneja, Albert Wilcox, Benjamin Joffe, Animesh Garg
ICRA 2026
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Code |
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Tweet |
Podcast
AMPLIFY is a novel framework that leverages large-scale video data by encoding visual dynamics into compact, discrete motion tokens derived from keypoint trajectories. Our modular approach decouples the challenges of learning what motion defines a task from how robots can perform it.
Ayush Agarwal*, Ansh Gandhi*, Jeremy A Collins, Omar Rayyan, Aryan Sarswat, Ranjani Koushik, Kaavya Menon, Masoud Moghani, Ajay Mandlekar, Animesh Garg
ICRA 2026
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Website
COBALT democratizes robot learning by letting hundreds of users worldwide teleoperate cloud‐hosted, vectorized simulators via everyday devices while a suite of metrics and a structured training curriculum ensure the collection of large, high-quality datasets for both simulated and real-world robots.
Hrishit Leen, Jeremy A Collins, Kunal Aneja, Nhi Nguyen, Priyadarshini Tamilselvan, Sri Siddarth
Chakaravarthy P, Animesh Garg
CoRL 2025 Workshop on Dexterous Manipulation (Spotlight)
Paper |
Poster
FLASH is a method for language-conditioned dexterous grasping that jointly models task intent and physical contact quality for robot hands.
Ezra Ameperosa*, Jeremy A Collins*, Mrinal Jain, Animesh Garg
ICRA 2025
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Code |
Website
RoCoDA is a data augmentation framework unifying the concepts of invariance, equivariance, and causality to enhance data augmentation for imitation learning, thus improving generalization and sample efficiency across manipulation tasks.
Krishnan Srinivasan, Jeremy A Collins, Eric Heiden, Ian Ng, Jeannette Bohg, Animesh Garg
ISRR 2024
Paper
DexMOTS leverages object-centric motion trajectories and differentiable simulation to efficiently learn dexterous manipulation policies, improving policy performance in contact-rich tasks.
Jeremy A Collins*, Cody Houff*, You Liang Tan*, Charles C Kemp
ICRA 2024, CoRL 2023 Demo
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Website |
Video
ForceSight is an RGBD-adapted, text-conditioned vision transformer. Given an RGBD image and a text prompt, ForceSight produces visual-force goals for a mobile manipulator, and successfully generalizes to novel environments and unseen object instances.
Jeremy A Collins, Cody Houff, Patrick Grady, Charles C Kemp
IROS 2023
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Video
ViPER is a model that leverages multiple sources of data to visually estimate contact pressure on robotic grippers, enabling precise manipulation in varied, uncontrolled environments.
Patrick Grady, Jeremy A Collins, Chengcheng Tang, Christopher D Twigg, James Hays, Charles C Kemp
WACV 2024, CVPR 2023 Demo
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Video
PressureVision++ visually estimates fingertip contact pressure using weak supervision and adversarial domain adaptation, enabling mixed reality interfaces with everyday surfaces.
Patrick Grady, Jeremy A Collins, Samarth Brahmbhatt, Christopher D Twigg, Chengcheng Tang, James Hays, Charles C Kemp
IROS 2022
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Code |
Video
VPEC is a model that infers and controls the pressure applied by soft robotic grippers using a single RGB image, enabling precise manipulation of small objects such as coins and microSD cards.
Anthony L Gunderman, Jeremy A Collins, Andrea L Myers, Renee T Threlfall, Yue Chen
RA-L 2022
Paper
We develop a tendon-driven soft robotic gripper with active force feedback that gently harvests blackberries, minimizing postharvest damage.
Developed an algorithm enabling a mobile robot to detect and follow objects, avoid obstacles, and navigate complex environments.
I made this to get some intuition for recursion. Did you know the "B." in Benoit B. Mandelbrot stands for Benoit B. Mandelbrot?
Aug. 2023 - Present
Ph.D. in Machine Learning
Aug. 2021 - May 2023
M.S. in Robotics | GPA: 4.00
Aug. 2017 - May 2021
B.S. in Mechanical Engineering, Minor in Mathematics | GPA: 4.00
Founding Member of Technical Staff (Jun. 2026 - Present)
Intern, Large Behavior Models Team (May 2025 - May 2026)
Volunteer Researcher (May 2023 - Aug. 2023)
Engineering Intern (Jul. 2021 - May 2022)
Mechanical Engineering Intern (May 2019 - Aug. 2019)
Application Development Intern (May 2018 - Aug. 2018)
Quality Control and Logistics (May 2015 - Aug. 2017)