Integrating Machine Vision with Robotic Arms

Can AI and LLMs Get Robots to Cooperate Smarter? Imagine going into a busy factory floor where robots are performing complicated tasks but also describing in real time what they're doing. This was the sci-fi dream now so well within grasp. This paper gives great insight into how LLMs and VLMs will reshape human-robot collaboration-particularly in high-consequence industries. 🔹 Research Focus Ammar N. Abbas (TU Dublin Computer Science) and Csaba Beleznai (AIT Austrian Institute of Technology) discussed how the integration of LLMs and VLMs into robotics will interpret natural language commands, understand inputs in the form of images, and explain internal processes in plain language. This approach is about creating interpretable systems, building trust, safety, and simplifying operations. 🔹 Language-Based Control LLMs are good at taking general instructions like "Pick up the red object" and turning them into very specific movements. The few-shot prompting allows learning to perform sophisticated trajectories for robots, without requiring thorough programming of the robot moves. The development decreases time spent in training and simultaneously enhances flexibility. 🔹 Context-Aware Perception By externalizing internal states, the robot alerts the operator in the event of an imminent collision or when something is missing from the environment. This form of transparency, in other words, not only builds trust but also allows to make quicker and more informed decisions, hence reducing down times and risk. 🔹 Integrating Input from Vision VLMs process sequential images to provide robots with enhanced spatial awareness. This capability enables tasks like sorting items by attributes, avoiding obstacles, and identifying safe zones for operations. 🔹 Robot Structure Awareness Equipping LLMs with knowledge of the physical structure of a robot, such as reach or mechanical limits, allows for superior task planning. For instance, it avoids overreaching and unsafe movements by robots while ensuring the accuracy and safety of the workplace. 🔹 Key Takeaway The framework illustrated industrial tasks like stacking, obstacle avoidance, and grasping through simulation in: - Accurate generation of control patterns - Real-time contextual reasoning and feedback. - Performing multi-step tasks successfully with both structural and visual data. 📌 Practical Applications   This research aims to make advanced robotics accessible to non-experts by bridging automation and collaboration. It promises faster deployment, enhanced safety, efficiency, and improved trust between human and robotic teams. 👉 How can AI and LLMs enhance decision-making in industrial robotics? What are the biggest challenges in implementing LLM-driven robotics? 👈 #ArtificialIntelligence #MachineLearning #AI #GenerativeAI #IndustrialAutomation #Robotics #SmartManufacturing Subscribe to my Newletter: https://lnkd.in/dQzKZJ79

🦾 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐢𝐧 𝐏𝐢𝐜𝐤-𝐚𝐧𝐝-𝐏𝐥𝐚𝐜𝐞 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬: 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐒𝐢𝐦𝐏𝐋𝐄 🦾 MIT researchers have developed SimPLE (Simulation to Pick, Localize, and placE), a groundbreaking solution for precise pick-and-place tasks in robotics. Unlike traditional methods, SimPLE enables robots to pick, regrasp, and place objects using computer-aided design (CAD) models without prior encounters with the objects. This innovative approach integrates vision and touch (visuotactile sensing) to achieve high accuracy, transforming unstructured object arrangements into organized setups. In testing, SimPLE achieved over 90% success in placing diverse objects, showcasing its potential to revolutionize industrial automation. Developed by MIT’s Manipulation and Mechanisms Lab (MCube) under the direction of Alberto Rodriguez, now at Boston Dynamics, SimPLE is a testament to the power of collaboration across generations of researchers. Combining tactile sensing and vision, this system provides a reliable alternative to AI methods and holds immediate practical applications in industries like manufacturing. It’s an impressive example of what we call "good old-fashioned engineering." Watch the full video here: https://lnkd.in/ey36DJaq

VLAs are the GPTs of robotics To understand how a VLA model works, it helps to think about how a person performs a task. We use our senses to perceive the world and our abilities to interact with it. A VLA-powered robot operates on a similar principle, built upon three essential pillars: Vision (The Eyes): This is the robot's ability to see and interpret its surroundings. Using cameras, the robot takes in "visual observations" to identify objects, understand their spatial relationships, and recognize the overall context of a scene. Language (The Ears and Brain): This is the robot's ability to understand human commands. By processing "natural language instructions," the robot can grasp complex, high-level requests without needing a pre-programmed script for every possible task. Action (The Hands): This is the robot's ability to turn understanding into physical movement. Through "robotic action generation," the model calculates the precise sequence of motor controls needed to move its grippers, arms, and other parts to successfully complete the instruction. Crucially, these three pillars don't work in isolation. They are fused together in a constant feedback loop, allowing the robot to see, understand, and act as a single, cohesive system. Consider the complex command "place the red mug next to the laptop onto the top shelf." Here’s how a VLA model breaks it down: Vision: The robot's camera sees the room, identifying the red mug, the laptop, and the top shelf. Language: The model understands the command's grammar and the spatial relationships: "next to the laptop" and "onto the top shelf." It connects these phrases to the objects identified by its vision. Action: The robot calculates and executes the sequence of movements to navigate, pick up the mug, and place it in the correct final location. Combining these three pillars into a single, intelligent system is the core purpose of a VLA model.

Impressed? A creative agency named Redpepper has engineered an AI-driven robot called “There’s Waldo.” This innovative robot can quickly find and identify Waldo using Google’s AutoML Vision technology. The underlying tech is quite intriguing—it operates on a Raspberry Pi and leverages the PYUARM Python library to control a UARM Metal robotic arm. The combination of cutting-edge AI and accessible hardware like Raspberry Pi showcases how advanced technologies can be integrated into fun and practical applications. The use of Google’s AutoML Vision service also highlights the power of machine learning in image recognition tasks. Here’s a breakdown of how it functions: 📌 The robotic arm captures an image of the scene and, with assistance from OpenCV, detects and isolates faces. 📌 These faces are then analyzed by Google’s AutoML Vision to determine if they match a pre-trained Waldo model. 📌 If a match is found with at least 95% confidence, the arm immediately points to the correct face. This step-by-step process underscores the effectiveness of combining various AI tools to create a seamless user experience. The accuracy and speed with which the robot identifies Waldo emphasize the potential of AI in solving complex visual tasks. #techviny #itleaders #technology #robotics #innovation #ArtificialIntelligence #ai

🟢 ROS 2 based Robot Perception Project for beginners: Robot Arm Teleoperation Through Computer Vision Hand-Tracking 🤖💡 (Full post: https://lnkd.in/ea8aumaf) 🤖🔧 Franka Emika Panda robot arm 🕞 Duration: 10 weeks 📷 Hand Tracking & Gesture Recognition: Leveraging Google MediaPipe and OpenCV to achieve seamless hand tracking and gesture recognition. The vision pipeline can adeptly track and decipher gestures from multiple hands simultaneously. 👋✨ 🏃♂️ Advanced Functionality: With PickNik Robotics's moveit_servo package, collision avoidance, singularity checking, joint position and velocity limits, and motion smoothing were implemented, ensuring precise and safe operation. 🛡️🔄 🎛 Custom ROS 2 Package Development: A tailored ROS 2 package for the Franka arm, involving intricate adjustments and fusion of URDF, SRDF, and other configuration files was developed. 🛠️🤖 🤖 Robust ROS 2 Implementation: The project boasts multiple ROS2 nodes and packages coded in both C++ and Python, ensuring versatility and efficiency in operation. 🐍🔨 💻 The system Graham developed is composed of three main nodes: handcv, cv_franka_bridge, and franka_teleop. The handcv node captures the 3D position of the user’s hands and provides gesture recognition. The cv_franka_bridge processes the information provided from handcv and sends commands to the franka_teleop node. The franka_teleop node runs an implementation of the moveit_servo package, enabling smooth real-time control of the Franka robot. 💪🚀 Project page: https://lnkd.in/ercAt5qz 🐱 GitHub: https://lnkd.in/eDb6gRyy Right now, gestures from both hands are captured, but only gestures from the right hand are used to control the robot. Here’s a list of the gestures the system recognizes and what they do: 👍 Thumbs Up (Start/Stop Tracking): This gesture is used to start/stop tracking the position of your right hand. It also allows you to adjust your hand in the camera frame without moving the robot. While the camera sees you giving a thumbs up, the robot won’t move, but once you release your hand, the robot will start tracking your hand. [thumbs_up] 👎 Thumbs Down (Shutdown): This gesture is used to stop tracking your hand and to end the program. You will not be able to give the thumbs up gesture anymore and will have to restart the program to start tracking your hand again. [thumbs_down] 👊 Close Fist (Close Gripper): This gesture will close the gripper of the robot. [closed_fist] 🤲 Open Palm (Open Gripper): This gesture will open the gripper of the robot. [open_palm] Credits: Graham Clifford completed this project as part of M.S. in Robotics at Northwestern University 🎓💼