Results 21 to 30 of about 257 (137)
CERBERUS in the DARPA Subterranean Challenge
Science Robotics, 2022 This article presents the core technologies and deployment strategies of Team CERBERUS that enabled our winning run in the DARPA Subterranean Challenge finals. CERBERUS is a robotic system-of-systems involving walking and flying robots presenting resilient autonomy, as well as mapping and navigation capabilities to explore complex underground ...
Marco Tranzatto +10 more
openaire +3 more sources
Advanced Materials Technologies, EarlyView.Aerosol Jet Printing (AJP) has emerged as a versatile additive manufacturing technique for high‐resolution, conformal, and multi‐material printing. This review highlights advances in printable materials, substrate compatibility, post‐processing, characterization, and process innovations, while critically discussing current challenges and future ...
Chandrachur Chatterjee +2 more
wiley +1 more source
A Review on Sensor Technologies, Control Approaches, and Emerging Challenges in Soft Robotics
Advanced Robotics Research, EarlyView.This review provides an introspective of sensors and controllers in soft robotics. Initially describing the current sensing methods, then moving on to the control methods utilized, and finally ending with challenges and future directions in soft robotics focusing on the material innovations, sensor fusion, and embedded intelligence for sensors and ...
Ean Lovett +5 more
wiley +1 more source
Polychip‐A High‐Throughput Droplet Microfluidics Platform for Interrogating Microbial Interactions
Advanced Science, EarlyView.Polychip, a fully integrated droplet microfluidics platform, enables high‐throughput, single‐cell resolution screening of polymicrobial interactions. By seamlessly combining six automated microfluidics operations on a single chip, the system accelerates antimicrobial discovery by 11 to 14 times compared to traditional robotic methods.
Jeong Jae Han +11 more
wiley +1 more source
Author Correction: Electrically pumped photonic integrated soliton microcomb
Nature Communications, 2019 The original version of this Article contained an error in the first sentence of the Acknowledgements, which incorrectly read ‘This publication was supported by Contract HR0011-15-C-0055 (DODOS) from the Defense Advanced Research Projects Agency (DARPA),
Arslan S. Raja +13 more
doaj +1 more source
DARPA's Impact on Artificial Intelligence
AI Magazine, 2020 The Defense Advanced Research Project Agency's (DARPA) mission is to make pivotal investments leading to research breakthroughs that support national security. DARPA artificial intelligence (AI) programs have emphasized the need for machines to perceive and interact with the world around them; to frame problems and to arrive at solutions and decisions ...
Scott Fouse +2 more
openaire +2 more sources
Advanced Science, EarlyView.Magnetoelectric nanoparticles (MENPs) enable fully wireless, minutely invasive neuromodulation, and potentially neural recording, by converting magnetic into electric and, conversely, electric into magnetic fields, respectively, at high spatiotemporal resolution.
Elric Zhang +14 more
wiley +1 more source
Advanced Science, EarlyView.A clinically translatable cell line can be engineered to produce leptin, encapsulated in biomaterial and safely implanted to produce and deliver the metabolic regulating protein within the body. Implantation of these encapsulated cells decreases the time it takes for mice and non‐human primates to adjust to circadian disruptions similar to those ...
Samantha T. Fleury +18 more
wiley +1 more source
DARPA's Role in Machine Learning
AI Magazine, 2020 Machine learning methods provide a way for artificial intelligence systems to learn from experience. This article describes four threads of machine learning research supported and guided by the Defense Advanced Research Projects Agency — probabilistic modeling for speech recognition, probabilistic relational models, the integration of multiple machine ...
Joshua Alspector, Thomas G. Dietterich
openaire +2 more sources
Advanced Intelligent Discovery, EarlyView.The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source