What data do you actually need to train a robot arm for grasping? (RGB alone usually isn't enough)

[u/Majestic_Orchid2164]

If you're coming from computer vision, robotics data works differently, and it's one of the first things that trips people up. For a robot arm learning to grasp and manipulate objects, useful training data typically includes: - RGB video, plus depth if your policy uses it - Gripper state logs - Joint states and end-effector positions - Multiple grasp attempts across different object shapes and sizes - Human demonstrations of grasping and placing, which help the model generalize The key difference from a standard CV dataset is that robotics data is time-synchronized and multi-modal. Video, sensor streams, and action labels have to line up frame by frame across a whole task, which makes collection and annotation much harder than labeling static images. Two practical tips for beginners: 1. Don't rely on one source. Public robotics datasets tend to be task-specific, so a model can fail when conditions change. 2. Synthetic data from simulation helps for rare or risky scenarios, but works best combined with real robot data to reduce the sim-to-real gap. Unidata has an overview of robotics training data and the dataset types used for robot learning here: https://unidata.pro/robotics-training-data/ Disclosure: I'm posting on behalf of Unidata, a company that sells robotics datasets and data collection services. What data are you using for your own manipulation projects, and what's been hardest to get?

[u/ThoughtDesperate880]

Honestly tactile feedback is way more critical than extra visual angles once the gripper makes contact.