This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao +4 more
wiley +1 more source
When touch is stressful: acute endocrine and behavioral responses of domestic rabbits to unfamiliar human handling. [PDF]
Součková M +4 more
europepmc +1 more source
Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki +2 more
wiley +1 more source
LLM‐Integrated Human–Robot Interaction System for Microrobots
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley +1 more source
Impact of wear position on dosimeter performance: measurement validity under simulated indoor illumination. [PDF]
de Vries SW +2 more
europepmc +1 more source
Emotions in motion: How fear and anger differently influence forward single-step initiation. [PDF]
Coudrat L +3 more
europepmc +1 more source
Applying Principles of Biomechanics of the Spine to Martial Arts: A Review on Balance of Stances in Goju-Ryu Karate-Do. [PDF]
Fiechter M, Pötzel T, Pfeifer ME.
europepmc +1 more source
Gait Optimization Control of Spinal Quadruped Robot Based on Deep Reinforcement Learning. [PDF]
Song G +5 more
europepmc +1 more source
Computational protocol for quantifying body-bending amplitude and period in Caenorhabditis elegans. [PDF]
Shi R, Sun Y, Xiao J, Di Z, Liu H.
europepmc +1 more source

