activity
20222026
most citedIMA-Catcher: An IMpact-Aware Nonprehensile Catching Framework based on Combined Optimization and Learning

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.RO2026

Neural Implicit Action Fields: From Discrete Waypoints to Continuous Functions for Vision-Language-Action Models

Haoyun Liu, Jianzhuang Zhao, Xinyuan Chang +11

Despite the rapid progress of vision-language-action (VLA) models, the prevailing practice of predicting action chunks as discrete waypoints remains structurally misaligned with th…

cs.RO2025

End-to-End Humanoid Robot Safe and Comfortable Locomotion Policy

Zifan Wang, Xun Yang, Jianzhuang Zhao +5

The deployment of humanoid robots in unstructured, human-centric environments requires navigation capabilities that extend beyond simple locomotion to include robust perception, pr…

cs.RO2025★ 1 cited

IMA-Catcher: An IMpact-Aware Nonprehensile Catching Framework based on Combined Optimization and Learning

Francesco Tassi, Jianzhuang Zhao, Gustavo J. G. Lahr +5

Robotic catching of flying objects typically generates high impact forces that might lead to task failure and potential hardware damages. This is accentuated when the object mass t…

cs.RO2024

A Combined Learning and Optimization Framework to Transfer Human Whole-body Loco-manipulation Skills to Mobile Manipulators

Jianzhuang Zhao, Francesco Tassi, Yanlong Huang +2

Humans' ability to smoothly switch between locomotion and manipulation is a remarkable feature of sensorimotor coordination. Leaning and replication of such human-like strategies c…

cs.RO2022

Impact-Friendly Object Catching at Non-Zero Velocity Based on Combined Optimization and Learning

Jianzhuang Zhao, Gustavo J. G. Lahr, Francesco Tassi +3

This paper proposes a combined optimization and learning method for impact-friendly, non-prehensile catching of objects at non-zero velocity. Through a constrained Quadratic Progra…

cs.RO2022

A Hybrid Learning and Optimization Framework to Achieve Physically Interactive Tasks with Mobile Manipulators

Jianzhuang Zhao, Alberto Giammarino, Edoardo Lamon +3

This paper proposes a hybrid learning and optimization framework for mobile manipulators for complex and physically interactive tasks. The framework exploits an admittance-type phy…