Bright Machines’ Post

Learn how to setup the CRX robot arm for image capture and training AI models for HACARUS Check in this short hands-on video.

Exciting new blog post alert! Find out "What AIs are not Learning (and Why)" in the latest article at arXiv:2404.04267v1. Learn about the limitations and opportunities for AI in robotics and how we can pave the way for a future where robotic AIs can learn experientially and serve people broadly. Check out the full post here: https://bit.ly/3VRTuyX.

🚦 New YouTube Video Alert! 🤖 Check out my latest video where I show you how to control a Quarky robot using traffic signs, powered by Pictoblox and AI. This innovative project combines the world of robotics with real-world applications like traffic management and AI-driven learning. 🚗💡 Watch the full video to see how you can program and control a robot with just traffic signs! Don’t forget to subscribe for more exciting tech projects and tutorials. 🔗 Watch here #Robotics #AI #Pictoblox #STEM #QuarkyRobot #TechTutorials #TrafficSignRecognition

🚀 Perception Upgrade for My Robot 🤖 After nailing down basic motion and navigation, I’m improving the robot’s perception abilities! 👀 Perception is how a robot transforms raw sensor data into useful information. One of the most valuable types is image data. Initially, the camera provides only a pixel stream, but the key is understanding if there are any objects the robot should recognize and interact with. In my case, object detection is the most practical task. I want my robot to know what’s in front of it and where those objects are in space. While there are existing object detection libraries, none felt quite right for my project. So, I wrote my own object detection package using open-source models! And here's where it gets interesting—I’m using a depth camera, which means I not only get color info but also the distance of each pixel from the camera. With this data, I convert detected objects into 3D point clouds, so the robot can understand both what and where the objects are in the environment. This feature will open up doors for more advanced capabilities like object tracking and person-following in the future. 🚶♂️🤖 #Robotics #AI #ObjectDetection #RobotVision #3DPointCloud #DepthSensing #Perception #ROS2 #MachineLearning

Atomic Force Microscopy (#AFM) provides detailed information on #surfacetopography, #roughness, and mechanical properties at the nanoscale. It can also measure #surface #forces, #ElectricalConductivity, #MagneticProperties, and material stiffness, making it a versatile tool for characterizing a wide range of materials.

Get the highest resolution images and the most accurate data autonomously, thereby accelerating your research. 🚀 Unlike others, Park FX40 takes care of everything automatically: from probe pick-up to landing to full autonomous scanning of the sample at a click of a button. It does this by infusing robotics, AI and machine learning into its groundbreaking FX system freeing researchers from manual operations. Park FX40 automates:  ✅ Probe Identification: Automatically recognizes and selects the appropriate probe for your samples. ✅ Probe Exchange: Seamlessly switches probes without manual intervention. ✅ Beam Alignment : Ensures optimal beam positioning for accurate measurements. To learn more about this incredible AFM, click here: https://lnkd.in/dJaabUf5 . . #ParkFX40 #AutonomousResearch #AdvancedImaging #DataAccuracy #RoboticsInScience #AIInResearch #MachineLearning #ResearchInnovation #AutomatedScanning #AFMTechnology #ProbeExchange #BeamAlignment #ResearchEfficiency #CuttingEdgeTechnology #ScientificDiscovery #ResearchAutomation #LabInstruments #HighResolutionImages #TechForResearchers #FutureOfScience

Among various models of AFM by Park System, FX40 have its amazing features.....

This year at NeurIPS, we gave an Oral presentation on a new AI diffusion method for automatically designing soft robot bodies and behaviors. DiffuseBot uses the same technique that powers image generators like Dall-E, but generates soft robots rather than images. We co-optimize shape, actuator placement, and control to solve tasks in passive dynamics, manipulation, and locomotion. One framework does it all. Because we insert differentiable physics directly into the computational pipeline, we don't need big datasets of robot designs to make this work --- which is great, because those datasets don't exist! DiffuseBot improves from its own trial and error, and because it's constantly testing what it generates, you don't have to worry about weird "hallucinations" or other common artifacts of generative AI. Watch the CSAIL video on the research: https://lnkd.in/edhpqkkG Or check out more details here: https://lnkd.in/erz2qPw7 Joint work with Tsun-Hsuan Wang, Juntian Zheng, Pingchuan Ma, Yilun Du, Byungchul Kim, Joshua B. Tenenbuam, Chuang Gan, and Daniela Rus

Integrating the TossingBot technology into a business network https://t.me/theunityhub can provide several benefits: Efficiency: TossingBot enables robots to handle arbitrary objects with accuracy and generalization, streamlining sorting and kitting processes in various industries. By efficiently placing objects beyond their kinematic range, robots can optimize pick-and-place scenarios, enhancing overall operational efficiency. Flexibility: The ability of TossingBot to learn from visual observations and trial and error allows robots to adapt to diverse object poses and properties. This flexibility enables robots to handle a wide range of objects without the need for extensive reprogramming, facilitating flexible and adaptable automation solutions.

Long horizon reasoning is fascinating, and how we can enable long-horizon reasoning is probably the motivating drive for why a lot of people — myself included — are so interested in robotics and AI. Today I’m going through an interesting paper doing some simple long horizon reasoning for robots: Points2Plans. We occasionally see truly impressive examples of AI doing long-horizon reasoning - AlphaGo wowed the world back in 2016 when it defeated world-champion Go player Lee Sedol. More recently, we see OpenAI releasing its new o1-series of models for advanced reasoning. The goal of long horizon reasoning is to perform some previously unseen, multi-step task which involves repeatedly taking actions and interacting with the world. https://lnkd.in/e8YJppn9