Revolutionizing Object Detection: Moonream 2 in Jeff the G1 Series
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In the latest episode of the Jeff the G1 series, the team delves into the fascinating world of object detection using cutting-edge technology. By employing a Vision Language Model (VLM) like Moonream 2, they are able to track objects with incredible precision and speed. This VLM, with its two billion parameters, allows for real-time changes in object descriptions and locations, revolutionizing the way objects are tracked in XY space. The team showcases the VLM's power through a user-friendly GUI, demonstrating its ability to detect a wide range of objects based on natural language descriptions. From microwaves to canned air, the VLM proves its versatility and accuracy in identifying objects with lightning speed.
Despite the impressive capabilities of the VLM, the team faces challenges such as optimizing the arm's movement speed and improving accuracy by adding colored tape to objects like the robotic hand. They also explore the impact of camera positioning on visibility and the need for depth measurements from the hand's perspective. Troubleshooting issues with power supply and port connections provide valuable insights into the internal workings of the Unit G1, shedding light on the complexities of robotics technology. Through meticulous testing and experimentation, the team gains a deeper understanding of the intricacies involved in object detection and tracking.
The team's dedication to pushing the boundaries of robotics technology is evident in their quest to retain SLAM technology while adjusting the camera angle for better visibility. By tackling these challenges head-on, they pave the way for future advancements in the field of robotics. The Jeff the G1 series serves as a testament to the team's relentless pursuit of innovation and their unwavering commitment to exploring the endless possibilities of VLM technology. With each episode, they bring us closer to a future where robots seamlessly interact with their surroundings, guided by the power of cutting-edge vision and language models.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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Viewer Reactions for A bigger brain for the Unitree G1- Dev w/ G1 Humanoid P.4
Boston Dynamics released a new model for Atlas related to visual perception and planning actions based on environmental changes
Suggestions for using visual tracking algorithms, updating occupancy maps, and splitting voxels for better object detection
Excitement about the small head of the robot and potential advancements in future models
Interest in using LiDAR point cloud for physical modeling and collision detection
Suggestions to integrate MoveIT in ROS 2 for grabbing objects
Ideas for integrating the project with Groot and solving path planning issues
Questions about hand pathfinding without ML and the weight of the robot's arm
Comments on the use of metal actuators in the joints and the potential cloud-based nature of robots
Concerns about AI alignment and the alignment of systems with the law of humanity
Appreciation for the creator's work and videos, with some users finding them inspiring and exciting
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