Rainier Labs

Imagine how you reach out to an object within your field of view, extend your arm to it, and grasp it within your fingers. This task, effortless for us, is rather more complicated for robots. Let's dive into the fascinating world of robotic grasping, where mathematics meets mechanics to mimic human manipulation. 6 DoF pose estimation using NVIDIA's Deep Object Pose Estimation neural net The Perception Challenge : At its core, robotic grasping begins with perception. Just as our eyes and brain work together to identify objects and their positions, robots use advanced computer vision algorithms to detect the 3D pose of objects. These algorithms act as the robot's "eyes," providing crucial information about what to grasp and where. Training robots to grasp objects involves using a perception model or computer vision algorithm to detect the 6 DoF (Degrees of Freedom) 3D pose of the object. This is often achieved using sophisticated neural networks like NVIDIA's D...

Imagine a future where robots handle everyday chores like dishwashing. No more dishes piling up in the sink. No more having to load and unload the dishwasher. Well, a professor from New Jersey, Usman Roshan and his CTO at 7XR , have been working on just this, for the past few months.  Autonomous robot dishwasher (ARD1) identifies plates, tap, water, sponge, sponge box, dirty vs clean plate, which plate to pick, as we see in the four views below - all of this helps it to understand what it is doing #robots #robotics #AI #deeplearning #machinelearning … pic.twitter.com/whSFr8Bh03 — Usman Roshan (@Deeplearner2) September 26, 2024 The X-post from 7xr.tech showcases a glimpse of our robotic future, featuring a couple of robotic arms from Elephant Robotics tackling dirty dishes. The system is completely vision based, with cameras near the end-effectors and learns from 60 videos of the CTO w...