New Embedded Vision Summit Demo Video: "INUITIVE Demonstration of the M4.51 Depth and AI Sensor Module Based on the NU4100 Vision Processor"
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A video describing our recent paper titled "Learning Neural Force Manifolds for Sim2Real Robotic Symmetrical Paper Folding" is available on YouTube at https://lnkd.in/g2Vu4Dfr
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Check out the innovative P1 biped robot by LimX Dynamics! Designed for stable walking in complex environments using advanced reinforcement learning, P1 features high-performance joint modules, real-time communication, and user-friendly software. With a Real2Sim2Real closed-loop system, robust neural network architecture, and a range of sensors, it's paving the way for efficient simulation-to-hardware deployment. Specs include a 48V battery, 1000W power, and up to 2 hours of operation. Follow this page for the latest development in AI #topaitools #MachineLearning #Robotics #AI #Innovation #Technology
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Sr. Program Manager Views, likes, comments and opinions are mine and do not reflect that of my employer or past employers.
Next-generation autonomous aircraft face many challenges. Artificial intelligence (AI) and machine learning (ML) applications enabling autonomous aircraft demand vastly increased processing performance but must also be certified safe. Check out Mercury's Safety Certifiable Computing whitepaper to learn more. #EuropeanRotors23 #InnovationThatMatters #Intel #AI #FVL
White Paper: Safety Certifiable Computing for Tomorrow's Avionics
amplify.mrcy.com
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embedded world: Altera optimizes FPGAs for edge AI - Embedded.com https://buff.ly/43VrrQY
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Senior Program Manager QA @ IBM | DevOps Test Automation, IBM Cloud, GEN AI, Quantum Computing, ML Data Science
Resolution Renaissance - The FeatUp algorithm! MIT researchers have created a system called “FeatUp” that lets algorithms capture all of the high- and low-level details of a scene at the same time. The FeatUp algorithm minimizes information loss and boosts the resolution of any deep network without compromising speed or quality. FeatUp helps practitioners understand their models and can improve a panoply of different tasks like object detection, semantic segmentation (assigning labels to pixels in an image with object labels), and depth estimation. It achieves this by providing more accurate, high-resolution features crucial for building vision applications ranging from autonomous driving to medical imaging.
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Autonomous vision-based low-altitude pipe tracking by multicopter: The validation of fully autonomous vision-based low-altitude pipe tracking has been done in GAZEBO simulation. The pipe geometry with various joints has been created in a simulation environment to test the performance of the algorithm during the turn. Milestone completed: -> Development of vision strategy for vision-based control of multicopter. -> Implementation of homographic transformation to improve the tracking performance. ->Design and heuristically tuning of sliding mode controller. To test the algorithm in a real scenario the Convolution neural network has been designed by amalgamation of DeepLabv3+ and Squeeze Net. The model has been deployed on the NVIDIA Jetson Xavier-NX.
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Researchers from National University of Singapore and The Johns Hopkins University present a novel probabilistic method for robotic skills learning in the 6D workspace including both position and orientation. The learned skills can be generalized to unseen objects, obstacles, and transfer among different robots. https://lnkd.in/gHaBZwZr Paper title: PRIMP: PRobabilistically-Informed Motion Primitives for Efficient Affordance Learning From Demonstration Authors: Sipu Ruan, Weixiao Liu, Xiaoli Wang, Xin Meng, and Gregory Chirikjian Video link: https://lnkd.in/g7uHzatG #ProbabilisticLogic #LearningfromDemonstration(LfD) #PathPlannning
PRIMP: PRobabilistically-Informed Motion Primitives for Efficient Affordance Learning From Demonstration
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Imagine controlling a full-sized humanoid with just a camera! Carnegie Mellon's latest breakthrough in teleoperation allows for seamless, real-time control of robots using only an RGB camera. Researchers used reinforcement learning and vast human motion datasets to allow the humanoid to mimic intricate human movements. From simple tasks like picking up objects to dynamic actions like boxing, this technology marks a new age in human-robot interaction!
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Working with Australia's most innovative Data Centre as a Service provider to power the digital economy
Coming soon to Netflix - Pimp my... Tractor? With 24 NVIDIA GPUs? In a perfect example of #AI bringing new tech to problem that's existed since the start of time, Carbon Robotics have announced the LaserWeeder™. A system using AI plant detection to quickly and effectively kill weeds with lasers. Not just a couple of weeds, but 5000 weeds per minute! With lasers! You can find out more below, but this is a fantastic example of machine vision and AI applications on the literal edge. Improving yield, reducing harm to the environment and being unbelievably awesome whilst doing so. Did I mention the tractor has Lasers! The model is currently trained 25 million images with inference occurring right on the tractor. https://lnkd.in/gKPeYxhq #whereAIthrives #ai #edgecompute
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To build a robot-assisted surgical system, dynamic uncertainties can be a critical issue when it comes to robot control. "Disturbance observer" is a common tool for disturbance estimation and compensation by taking all uncertainties as disturbances, but this will refuse human-robot interaction since the human-applied force will also be regarded as a disturbance by the observer! #Iterative #learning for #gravity #compensation can be a promising way to solve this problem when gravity compensation is the main concern. In our newest publication, we present a gravity iterative learning (Git) scheme in Cartesian space for gravity compensation that integrates with an impedance controller. The integration of Git with an impedance controller allows for seamless human-robot interactions. This framework promises to improve the precision and safety of surgical robots by ensuring accurate trajectory tracking and set-point regulation without the need for high impedance gains. Teng Li, Amir Zakerimanesh, Yafei Ou, Armin Badre, and Mahdi Tavakoli, Iterative Learning for Gravity Compensation in Impedance Control, IEEE/ASME Transactions on #Mechatronics, 2024. https://lnkd.in/gUCvte4b https://lnkd.in/grsfJyRM
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