Jeremy Collins

Hey! I'm Jeremy, and I'm 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, designing efficient data augmentation algorithms, and building accessible platforms for large-scale teleoperation, with the goal of enabling robots to generalize to new tasks and environments and operate reliably in the real world.

Publications

RoCoDA: Counterfactual Data Augmentation for Data-Efficient Robot Learning from Demonstrations

Ezra Ameperosa, Jeremy A Collins, Mrinal Jain, Animesh Garg
ICRA 2025
Paper | 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.

DexMOTS: Dexterous Manipulation with Differentiable Simulation

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.

ForceSight: Text-Guided Mobile Manipulation with Visual-Force Goals

Jeremy A Collins, Cody Houff, You Liang Tan, Charles C Kemp
ICRA 2024, CoRL 2023 Demo
Paper | 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.

Visual Contact Pressure Estimation for Grippers in the Wild

Jeremy A Collins, Cody Houff, Patrick Grady, Charles C Kemp
IROS 2023
Paper | 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.

PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images

Patrick Grady, Jeremy A Collins, Chengcheng Tang, Christopher D Twigg, James Hays, Charles C Kemp
WACV 2024, CVPR 2023 Demo
Paper | Website | Video

PressureVision++ visually estimates fingertip contact pressure using weak supervision and adversarial domain adaptation, enabling mixed reality interfaces with everyday surfaces.

Force/Torque Sensing for Soft Grippers using an External Camera

Jeremy A Collins, Patrick Grady, Charles C Kemp
ICRA 2023
Paper | Code | Video

VFTS is a deep learning approach that uses a wrist-mounted camera to visually estimate 6-axis force and torque on robotic grippers.

Visual Pressure Estimation and Control for Soft Robotic Grippers

Patrick Grady, Jeremy A Collins, Samarth Brahmbhatt, Christopher D Twigg, Chengcheng Tang, James Hays, Charles C Kemp
IROS 2022
Paper | 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.

Tendon-Driven Soft Robotic Gripper for Blackberry Harvesting

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.

Selected Personal Projects

Video Prediction Using Stable Diffusion

Code | Video

Trained a video prediction model that autoregressively predicts and then denoises embeddings from Stable Diffusion.

Learning Robotic Tasks from Video Demonstrations

Code | Video

Implemented several models to learn a control policy from expert video demonstrations, achieving an 85% success rate in pick-and-place tasks.

Distracted Driver Detection

Code | Video

Created a model that visually classifies driver behavior in real-time.

Midnight Stretch: A Semi-Autonomous Robotic Caregiver

Code | Video

Midnight Stretch is a robotic system that autonomously detects falls in older adults. Features include human motion detection, voice activation, and a web interface for video calls.