5 citations · 5 across the 12 of their papers we have counts for
13 papers
AGRO-SUVIDE: Agentic Robotics for Surgical Viscoelastic Debridement
Shutong Jin, Ziyang Chen, Preethi Satish +5
Augmented dexterity has the potential to reduce the fatigue experienced by surgeons during repetitive surgical tasks. In this paper, we propose the first AGentic RObotics framework…
MACAW: Reliable And Efficient Surgical Debridement Using Monocular Adaptive Compact Attention Windows
Ziyang Chen, Shutong Jin, Preethi Satish +6
Augmenting the dexterity of human surgeons has the potential to free them from tedious subtasks. We consider debridement (removal of diseased or dead tissue fragments), which is ch…
Adversarial Vulnerabilities of Learned Telesurgery Policies
Shutong Jin, Ziyang Chen, Preethi Satish +3
While not yet in clinical deployment, learning-based policies are increasingly considered to augment the dexterity of human surgeons in robot-assisted surgery. Can the end-to-end m…
Speculative Policy Orchestration: A Latency-Resilient Framework for Cloud-Robotic Manipulation
Chanh Nguyen, Shutong Jin, Florian T. Pokorny +1
Cloud robotics enables robots to offload high-dimensional motion planning and reasoning to remote servers. However, for continuous manipulation tasks requiring high-frequency contr…
RoboLight: A Dataset with Linearly Composable Illumination for Robotic Manipulation
Shutong Jin, Jin Yang, Muhammad Zahid +1
In this paper, we introduce RoboLight, the first real-world robotic manipulation dataset capturing synchronized episodes under systematically varied lighting conditions. RoboLight…
ReLumix: Extending Image Relighting to Video via Video Diffusion Models
Lezhong Wang, Shutong Jin, Ruiqi Cui +3
Controlling illumination during video post-production is a crucial yet elusive goal in computational photography. Existing methods often lack flexibility, restricting users to cert…
Physically-based Lighting Generation for Robotic Manipulation
Shutong Jin, Lezhong Wang, Ben Temming +1
In this paper, we propose the first framework that leverages physically-based inverse rendering for novel lighting generation on existing real-world human demonstrations of robotic…
R900: Understanding the Cost-Effectiveness of Random Exploration from 900 Hours of Robotic Data Collection
Shutong Jin, Axel Kaliff, Ruiyu Wang +2
Data scarcity presents a key bottleneck for imitation learning in robotic manipulation. In this paper, we focus on random exploration data-actions and video sequences produced auto…
One-Shot Federated Learning with Classifier-Free Diffusion Models
Obaidullah Zaland, Shutong Jin, Florian T. Pokorny +1
Federated learning (FL) enables collaborative learning without data centralization but introduces significant communication costs due to multiple communication rounds between clien…
PACA: Perspective-Aware Cross-Attention Representation for Zero-Shot Scene Rearrangement
Shutong Jin, Ruiyu Wang, Kuangyi Chen +1
Scene rearrangement, like table tidying, is a challenging task in robotic manipulation due to the complexity of predicting diverse object arrangements. Web-scale trained generative…
Feature Extractor or Decision Maker: Rethinking the Role of Visual Encoders in Visuomotor Policies
Ruiyu Wang, Zheyu Zhuang, Shutong Jin +3
An end-to-end (E2E) visuomotor policy is typically treated as a unified whole, but recent approaches using out-of-domain (OOD) data to pretrain the visual encoder have cleanly sepa…
RealCraft: Attention Control as A Tool for Zero-Shot Consistent Video Editing
Shutong Jin, Ruiyu Wang, Florian T. Pokorny
Even though large-scale text-to-image generative models show promising performance in synthesizing high-quality images, applying these models directly to image editing remains a si…
How Physics and Background Attributes Impact Video Transformers in Robotic Manipulation: A Case Study on Planar Pushing
Shutong Jin, Ruiyu Wang, Muhammad Zahid +1
As model and dataset sizes continue to scale in robot learning, the need to understand how the composition and properties of a dataset affect model performance becomes increasingly…