Labrador Retriever Robot: an Assistive Robot for Elderly

Labrador Retriever Robot: an Assistive Robot for Elderly

Labrador Systems, a company that makes assistive home robots, recently made Labrador Retriever which is an autonomous service robot to make life easier for senior adults or people with disabilities to move or "retrieve" things at home. Image credit: Labrador Systems Initially, the robot should be trained to build a map for the home. This training is done using a set of routes and stops (places that the robot should go, such as by the fridge) based on the customer's needs and preferences. Image credit: Labrador Systems Then the robot can navigate through the home autonomously using 3D vision and…
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Controlling a Robotic Arm with Brain

Controlling a Robotic Arm with Brain

Two research groups at EPFL (École polytechnique fédérale de Lausanne) supervised by Prof. Aude G. Billard, and Prof. José del R. Millán developed a machine learning (ML) algorithm that can learn from the patient's thoughts and enables to control a robot's movements based on electrical signals from the brain. Image credit: EPFL (École polytechnique fédérale de Lausanne) This research has potential applications to help tetraplegic patients who are unable to speak or perform any movement to independently perform activities of daily living (ADL). Key points: Patients move the robot with their thoughts, and no auditory or tactile feedback is needed.The…
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a Bioinspired Advanced Neural Control for Autonomous Walking Robots

a Bioinspired Advanced Neural Control for Autonomous Walking Robots

Researchers from BRAIN-lab at VISTEC in Thailand developed a bioinspired advanced neural control with proactive behavior learning and short-term memory for autonomous walking robots that enables them to traverse complex terrain. Image credit: BRAIN-lab at VISTEC The proposed control consists of three main modular neural mechanisms to create insect-like gaits, adapt robot joint movement individually with respect to the terrain during the stance phase using only the torque feedback, and generate a short-term memory for proactive obstacle avoidance during the swing phase. Their proposed control: does not require robot and environmental models, exteroceptive feedback, or multiple learning trialsdepends only on…
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