activity
20142026
most citedCovariance Matrix Adaptation for the Rapid Illumination of Behavior Space

121 citations · 654 across the 70 of their papers we have counts for

collaborators

76 papers

cs.RO2026

Red-Teaming of Vision-Language-Action Models via Quality Diversity Environment Generation

Shihan Zhao, Henry Chen, Ya-Chuan Hsu +7

A long-standing goal in robotics is to develop generalist robots that can perform tasks across a wide range of settings, flexibly specified with images and text. Vision-Language-Ac…

cs.RO2026

FELT: Generating Tactile Signals from Vision for Visuo-Tactile Manipulation

Zinan Li, Yiyang Ling, Yuming Gu +8

Tactile sensing can improve performance in contact-rich robot manipulation. However, learning policies that use real tactile feedback typically requires demonstrations collected wi…

cs.AI2026

Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization

Ayano Hiranaka, Ya-Chuan Hsu, Stefanos Nikolaidis +2

AI assistants in human-AI collaboration often correct suboptimal human actions through behavioral feedback (e.g., alerts or steering-wheel nudges in assistive driving). Such interv…

cs.RO2026

Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies

Siddharth Srikanth, Freddie Liang, Ya-Chuan Hsu +9

Vision-Language-Action (VLA) models have significant potential to enable general-purpose robotic systems for a range of vision-language tasks. However, the performance of VLA-based…

cs.RO2026

Improving through Interaction: Searching Behavioral Representation Spaces with CMA-ES-IG

Nathaniel Dennler, Zhonghao Shi, Yiran Tao +3

Robots that interact with humans must adapt to individual users' preferences to operate effectively in human-centered environments. An intuitive and effective technique to learn no…

cs.MA2026

Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding

Yulun Zhang, Varun Bhatt, Matthew C. Fontaine +2

Multi-Agent Path Finding (MAPF) aims to move agents from their start to goal vertices on a graph. Lifelong MAPF (LMAPF) continuously assigns new goals to agents as they complete cu…