activity
20242026
collaborators

20 papers

cs.RO2026

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

Zinan Li, Yiyang Ling, Yuming Gu +8

The sense of touch is central to manipulation, especially when vision is occluded or ambiguous. Although combining vision and touch improves manipulation, learning robust visuo-tac…

cs.CL2026

Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models

Siddharth Srikanth, Varun Bhatt, Boshen Zhang +5

Understanding how humans collaborate and communicate in teams is essential for improving human-agent teaming and AI-assisted decision-making. However, relying solely on data from l…

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.LG2026

Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces

Bryon Tjanaka, Henry Chen, Matthew C. Fontaine +1

Quality diversity (QD) optimization searches for a collection of solutions that optimize an objective while attaining diverse outputs of a user-specified, vector-valued measure fun…

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…