A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
unity
machine-learning
deep-learning
computer-vision
robotics
manipulation
perception
physics-simulation
pose-estimation
robotics-simulation
simulation
ur3-robot-arm
ros
urdf
motion-planning
synthetic-data
trajectory-generation
tutorial
model-training
autonomy
Обновлено 2022-04-13 20:50:31 +03:00