Robot control: Difference between revisions

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Added subsection on Cognibotics under Artificial intelligence, describing their sensorimotor control approach using Juliet&Romeo. Included reference to official source.
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Reverted 1 edit by Jncswe (talk): Commercial promotion sourced to the company's website
 
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==== Boston Dynamics' robots ====
[[Boston Dynamics|Boston Dynamic’s]] “Spot” is an autonomous robot that uses four sensors and allows the robot to map where it is relative to its surroundings. The navigational method is called [[simultaneous localization and mapping]], or “SLAM” for short. Spot has several operating modes and depending on the obstacles in front of the robot, it has the ability to override the manual mode of the robot and perform actions successfully. This is similar to other robots made by Boston Dynamics, like the “Atlas”, which also has similar methods of control. When the “Atlas” is being controlled, the control software doesn’t explicitly tell the robot how to move its joints, but rather it employs mathematical models of the underlying physics of the robot’s body and how it interacts with the environment”. Instead of inputting data into every single joint of the robot, the engineers programmed the robot as a whole, which makes it more capable to adapt to its environment. The information in this source is dissimilar to other sources, except the second source, because robots vary so much depending on the situation.<ref>{{Cite web |title=How Boston Dynamics Is Redefining Robot Agility - IEEE Spectrum |url=https://spectrum.ieee.org/how-boston-dynamics-is-redefining-robot-agility |access-date=2024-03-01 |website=[[IEEE]] |language=en}}</ref>
 
==== Cognibotics and sensorimotor control ====
[[Cognibotics]], a robotics company based in Sweden, has introduced a control paradigm based on real-time sensorimotor feedback and deterministic physics-based models. Their platform, known as Juliet&Romeo, is engineered for industrial automation and aims to bridge the gap between rule-based systems and AI-driven control architectures. Unlike systems that depend on large datasets or deep learning inference, Cognibotics emphasizes low-latency, real-time performance optimized for tasks such as high-speed picking, machining, and dynamic motion compensation. Their systems are designed to run efficiently on standard hardware, enabling practical deployment in environments with demanding motion requirements.<ref>{{Cite web |title=Juliet & Romeo – A Modern Stack for Industrial Automation |url=https://www.cognibotics.com/en/articles/juliet-romeo-modern-automation-development |access-date=2025-05-29 |website=cognibotics.com |language=en}}</ref>
 
 
==See also==