Humanoid says its KinetIQ Ascend approach can reach 99.9% manipulation reliability at human speed and beyond for industrial ...

Mobile robots are increasingly deployed in applications ranging from household cleaning to hazardous industrial inspection, ...

Sichkar V. N. "Reinforcement Learning Algorithms in Global Path Planning for Mobile Robot", 2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), Sochi, ...

WEST LAFAYETTE, Ind. — The human brain constantly makes decisions without us noticing. It requires minimal power to move our bodies in the desired direction or avoid an object. A Purdue University ...

Architecture Modular pipeline with object detection, mapping, and planning End-to-end policy network End-to-end framework without middle modules, avoiding error ...

These include such learning paradigms as Q-Learning and the Deep Q-Networks setups. Reinforcement Learning paradigms essentially aim at teaching robots to undertake certain actions that will be used ...

Embodied Artificial Intelligence (EAI) integrates artificial intelligence into physical entities like robots, endowing them with the ability to perceive, learn from, and dynamically interact with ...

Abstract: The paper aims to present the results of an assessment of adherence to the Deep Q-learning algorithm, applied to a vehicular navigation robot. The robot's job was to transport parts through ...

Magnetic soft continuum robots have received interest in diverse fields, because of their active steering and remote control capabilities. However, a more challenging task is the development of ...

Navigating safely and efficiently in dense crowds remains a challenging problem for mobile robots. The interaction mechanisms involved in collision avoidance require robots to exhibit active and ...