A multimodal foundation model-enabled agent for human–robot collaboration in construction

Safe human–robot collaboration remains a critical challenge in manufacturing. Traditional safety approaches, such as cages and proximity sensors, are often insufficient for dynamic human interaction. This paper presents a digital twin approach using deep learning for collision avoidance in industrial collaborative robot manipulation.

Industrial-grade robust robot vision system for screw detection and removal under uneven conditions.

Distance–area–IoU fusion loss for better neuro-symbolic robot manipulation

Design and evaluation of a tendon-and-linkage hybrid-driven humanoid dexterous hand

Appliances Describe Themselves: Part–Function–State Modeling for Appliance Manipulation Planning

CISMG-Nav: A Cross-Task Incremental Semantic Memory Graph-Driven Vision-and-Language Navigation Framework

ReaDy-Go: Real-to-Sim Dynamic 3D Gaussian Splatting Simulation for Environment-Specific Visual Navigation With Moving Obstacles

Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most existing approaches rely on large collections of robot demonstrations, which are costly. This survey covers scalable vision-language-action learning using human-centric data, particularly human videos, as an alternative to expensive robot demonstrations.

Vision-Language-Action (VLA) models have recently shown strong potential for robot learning by following language instructions. However, in practice, language alone is often insufficient to precisely convey human intent.