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Helping soft robots turn rigid on demand


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IMAGE: A simulated soft robot controlled to reach the same target (red dot) while acting either soft (left) or stiff (right).
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Credit: Courtesy of James Bern and Daniela Rus
Imagine a robot.
Perhaps you ve just conjured a machine with a rigid, metallic exterior. While robots armored with hard exoskeletons are common, they re not always ideal. Soft-bodied robots, inspired by fish or other squishy creatures, might better adapt to changing environments and work more safely with people.
Roboticists generally have to decide whether to design a hard- or soft-bodied robot for a particular task. But that tradeoff may no longer be necessary. ....

Daniela Rus , Daniel Ackerman , Artificial Intelligence Laboratory , News Office , International Conference On Soft Robotics , James Bern , Soft Robotics , Erna Viterbi Professor , Electrical Engineering , Computer Science , Mathematics Statistics , Algorithms Models , Technology Engineering Computer Science , Multimedia Networking Interface Design , Software Engineering , Theory Design , Research Development , Robotry Artificial Intelligence , டேனீலா ரஸ் , டேனியல் அக்கர்மன் , செயற்கை உளவுத்துறை ஆய்வகம் , செய்தி அலுவலகம் , சர்வதேச மாநாடு ஆன் மென்மையான ரோபாட்டிக்ஸ் , ஜேம்ஸ் பெர்ன் , மென்மையான ரோபாட்டிக்ஸ் , ஏர்ன விட்டர்பி ப்ரொஃபெஸர் ,

"Liquid" machine-learning system adapts to changing conditions | MIT News | Massachusetts Institute of Technology


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Image: Jose-Luis Olivares, MIT
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MIT researchers have developed a type of neural network that learns on the job, not just during its training phase. These flexible algorithms, dubbed “liquid” networks, change their underlying equations to continuously adapt to new data inputs. The advance could aid decision making based on data streams that change over time, including those involved in medical diagnosis and autonomous driving.
“This is a way forward for the future of robot control, natural language processing, video processing any form of time series data processing,” says Ramin Hasani, the study’s lead author. “The potential is really significant.” ....

Radu Grosu , Ramin Hasani , Daniela Rus , Alexander Amini , Mathias Lechner , Artificial Intelligence Laboratory , Vienna University Of Technology , Technology Austria , Institute Of Science , National Science Foundation , Austrian Science Fund , Erna Viterbi Professor , Electrical Engineering , Computer Science , Vienna University , Electronic Components , Time Series Analysis , Liquid Network , ராமின் ஹசானி , டேனீலா ரஸ் , அலெக்சாண்டர் அமினி , மத்தியாஸ் லெக்நர் , செயற்கை உளவுத்துறை ஆய்வகம் , வியன்னா பல்கலைக்கழகம் ஆஃப் தொழில்நுட்பம் , தொழில்நுட்பம் ஆஸ்ட்ரியா , நிறுவனம் ஆஃப் அறிவியல் ,