papers

Publications (15)

cs.RO2021

Towards Latent Space Based Manipulation of Elastic Rods using Autoencoder Models and Robust Centerline Extractions

Jiaming Qi, Guangfu Ma, Peng Zhou +3

The automatic shape control of deformable objects is a challenging (and currently hot) manipulation problem due to their high-dimensional geometric features and complex physical pr…

cs.RO2022

Adaptive Finite-Time Model Estimation and Control for Manipulator Visual Servoing using Sliding Mode Control and Neural Networks

Haibin Zeng, Yueyong Lyu, Jiaming Qi +3

The image-based visual servoing without models of system is challenging since it is hard to fetch an accurate estimation of hand-eye relationship via merely visual measurement. Whe…

cs.RO2020

A Lyapunov-Stable Adaptive Method to Approximate Sensorimotor Models for Sensor-Based Control

David Navarro-Alarcon, Jiaming Qi, Jihong Zhu +1

In this article, we present a new scheme that approximates unknown sensorimotor models of robots by using feedback signals only. The formulation of the uncalibrated sensor-based re…

cs.RO2026

Failure-Aware Bimanual Teleoperation via Conservative Value Guided Assistance

Peng Zhou, Zhongxuan Li, Jinsong Wu +7

Teleoperation of high-precision manipulation is con-strained by tight success tolerances and complex contact dy-namics, which make impending failures difficult for human operators…

cs.RO2023

Adaptive Shape Servoing of Elastic Rods using Parameterized Regression Features and Auto-Tuning Motion Controls

Jiaming Qi, Guangtao Ran, Bohui Wang +4

The robotic manipulation of deformable linear objects has shown great potential in a wide range of real-world applications. However, it presents many challenges due to the objects'…

cs.RO2026

Think Proprioceptively: State-Grounded Visual Token Selection for VLA Policies

Fangyuan Wang, Peng Zhou, Jiaming Qi +4

Vision-language-action (VLA) models typically inject proprioception only as a late conditioning signal, preventing robot state from grounding instruction understanding or directing…