Junxiang Wang

PhD student @CMU_Robotics | BS/MS @JohnsHopkins

[Hongyi Chen @chen_hongyi_]

How far can we push dexterous robot manipulation with human video-only supervision and minimal assumptions?
🚫 No teleop. 🚫 No wearables. 🚫 No external sensors. 🚫 No robot demos.
Introducing VIDEOMANIP: 🎥 Just monocular RGB, 🌍 in-the-wild human video → dexterous robot manipulation 🤚[1/6]

[CMU Robotics Institute @CMU_Robotics]

Robotic Caregiving & Human Interaction Lab researchers are developing assistive robots that use natural conversation & adapt to human preferences!

Led by @wang_junxiang_, the system enables robots to listen & adjust movements based on user input. 🦾

https://t.co/F3ZzqYFf7T

[JHU Computer Science @JHUCompSci]

Johns Hopkins roboticists—including alumni @JieYingWu and @wang_junxiang_—explore new ways of using @IntuitiveSurg’s da Vinci surgical system, from an open-source research kit to remote surgery improvements. Learn more: https://t.co/lxVewHJNeN

[Junxiang Wang @wang_junxiang_]

This project would not have been possible without the support of my advisor, @ZackoryErickson, and my collaborators Barış and Rana from Honda Research Institute.

[Junxiang Wang @wang_junxiang_]

How to convey a robot’s intent and motion such that anyone can understand?

Our #CoRL2025 paper introduces CoRI, a task- and robot-agnostic pipeline that communicates any robot’s intent, given a planned physically-assistive trajectory.
https://t.co/ENrOhbPrNQ
🧵

[Junxiang Wang @wang_junxiang_]

Join us at #CoRL2025 on 9/30 for our spotlight (3:30pm-4:30pm) and poster (5:00pm-6:30pm)!
Check more videos and generated communications on our website: https://t.co/ENrOhbPrNQ, and more details in our paper: https://t.co/5ioD7CFDEe

[Alexis Hao @hao_alexis]

Introducing FMVP: a method that adapts to natural arm motions during robot-assisted dressing.

Pre-trained on vision in sim, fine-tuned with limited real-world vision+force data, and tested in a 12-user, 264-trial study, FMVP is robust across garments and motions. #CoRL2025

[Divyam Goel @divyamgo10]

How do we discover a robot's failure modes before deploying it in the real world? Standard benchmarks often don't capture the full picture, leaving policies vulnerable to plausible variations in object shape.

Thrilled that our work, "Geometric Red-Teaming for Robotic Manipulation," has been accepted as an oral presentation at #CoRL2025! We introduce a framework to automatically find these geometric blindspots.
https://t.co/mh84bi7Wbp
🧵

[Hokin Deng @DengHokin]

#embodied All forms of biological intelligence are grounded movements🏃‍♂️ muscles & motor neurons 🧠 emerge before visual cortex & rods & cones in eyes 👁️

Building monocular better-than-mocap-studio #video2motion is our critical step towards human embodied intelligence.

[Yufei Wang @YufeiWang25]

Introducing ArticuBot🤖at #RSS2025, in which we learn a single policy for manipulating diverse articulated objects across 3 robot embodiments in different labs, kitchens & lounges, achieved via large-scale simulation and hierarchical imitation learning.
https://t.co/A0SZbAzhAh
🧵

[Hongyi Chen @chen_hongyi_]

[1/7] Teaching dexterous robot hands to perform functional grasps usually needs hours of teleoperation, manual labeling, or pre-scanning object meshes.

Not anymore.
🔥We are excited to introduce Web2Grasp that learns functional multi-finger grasps straight from web images of human hand-object interactions (HOI).
No human demos. No object scans. Just web images.
👉https://t.co/lOcWgMOpu5
@CMU_Robotics @CarnegieMellon