Results 231 to 240 of about 3,528,498 (350)

Tree-based machine learning methods for predicting vehicle insurance claim size. [PDF]

Front Big Data
Terefe EM, Aga MA.
europepmc   +1 more source

Multimodal Locomotion in Insect‐Inspired Microrobots: A Review of Strategies for Aerial, Surface, Aquatic, and Interfacial Motion

Advanced Robotics Research, EarlyView.
This review identifies key design considerations for insect‐inspired microrobots capable of multimodal locomotion. To draw inspiration, biological and robotic strategies for moving in air, on water surfaces, and underwater are examined, along with approaches for crossing the air–water interface.
Mija Jovchevska, Kaushik Jayaram, Nicole Xu   +2 more
wiley   +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Motorized Two-Wheeled Vehicles Contribute Disproportionately to the Increase in Pandemic-Period Road Traffic Fatalities in New York State. [PDF]

Int J Environ Res Public Health
Pressley JC, Aziz Z, Hines L, Guzman J, Pawlowski E, Bauer M.   +5 more
europepmc   +1 more source

A Soft Robotic Fish With a Dielectric Elastomer Actuator Body and Negative Stiffness Spine

Advanced Robotics Research, EarlyView.
This work introduces a bio‐mimetic soft robotic fish driven by fiber‐reinforced dielectric elastomer actuators integrated as its body. By prestretching this active skin against a flexible spine, a negative stiffness system is created, enabling large‐amplitude bending.
Markus Koenigsdorff, Simon Holzer, Stefania Konstantinidi, Yoan Civet, Yves Perriard   +4 more
wiley   +1 more source

Robotic Control for Human–Robot Collaborative Assembly Based on Digital Human Model and Reinforcement Learning

Advanced Robotics Research, EarlyView.
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, Hongzhou Ai, Wenjun Xu, Zude Zhou, Lihui Wang   +4 more
wiley   +1 more source

Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models

Advanced Robotics Research, EarlyView.
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, Ilche Georgievski, Marco Aiello   +2 more
wiley   +1 more source

Police Pursuit Fatalities in the US, 2009 to 2023.

JAMA Netw Open
Hendrix A, Martin T, Gazzetta J, Nunez J.   +3 more
europepmc   +1 more source

An automated decision making framework for modern vehicles CO₂ emissions using multi modal engine telemetry and feature interpretability. [PDF]

Sci Rep
Saraswat SK, Abdullah M, Habelalmateen MI, Vivek V, Sahu PK, Pant R, Sharma S, Shojonov M, Madaminov B.   +8 more
europepmc   +1 more source

Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Advanced Robotics Research, EarlyView.
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang, Ganesh Arivoli, Huzaifa Unjhawala, Luning Bakke, Radu Serban, Dan Negrut   +5 more
wiley   +1 more source