Train an ACT Policy for the SO-101 Robot with LeRobot скачать в хорошем качестве

Train an ACT Policy for the SO-101 Robot with LeRobot

📜Get repo access at https://Trelis.com/ADVANCED-robotics 🗝️ Get Trelis Multi-Repo Bundle (https://Trelis.com/multi-repo-bundle) Access all SEVEN Trelis Github Repos (-robotics, -vision, -evals, -fine-tuning, -inference, -voice, -time-series) Support via Github Issues & Trelis’ Private Discord Tip: If you subscribe here on YouTube, click the bell to be notified of new vids Credit to Rohan Sharma (https://x.com/rs545837) for assistance exploring ACT and GR00T. 💡 Done-for-you Custom Fine-tuning Services Learn More: https://trelis.com/fine-tuning-services/ 💸 Starting a New Project/Venture? Apply for a Trelis Grant: https://trelis.com/trelis-ai-grants/ 📧 Get Trelis AI Tutorials by Email Subscribe on Substack: https://trelis.substack.com Video Links: ACT Paper: https://arxiv.org/abs/2304.13705 LeRobot Library: https://github.com/huggingface/lerobot TIMESTAMPS: 00:00 Introduction to Training the SO-101 Robot with ACT 00:21 Overview of the Video Series 01:16 Scripts and Repo Access: Trelis.com/ADVANCED-robotics 01:57 Cloning and Installing LeRobot Libraries 06:07 Connecting and Configuring the Robots 08:53 Calibrating the Motors and Arms 12:33 Teleoperation Setup 18:04 PID Controller Calibration 27:10 Recording and Managing Data 39:05 Training the ACT Model 44:33 Style settings and KL Weight (ADVANCED) 49:06 Running Training on a Mac (or cpu) 50:54 Setting Up Validation and Output Directories 53:44 Running Training on Mac and Handling Issues 55:31 Monitoring Training Progress 57:25 Calculating Training S teps and Epochs 58:28 Analyzing Training and Validation Loss 01:04:02 Setting Up Training on GPU 01:08:19 Connecting to Remote Host and Cloning Repo 01:12:48 Running Training on CUDA 01:14:48 Handling Issues Running on CUDA 01:23:28 Inspecting Results after Running on CUDA 01:27:04 Evaluating Model Performance 01:28:37 Replay and Evaluation of Training Examples 01:35:21 Challenges with Generalization and Data Requirements 01:36:28 Using Image Augmentations and Jitter 01:37:08 Deciding Number of Rollout Steps 01:40:06 Ensembling Predictions for Smoother Trajectories 01:43:43 Conclusion and Next Steps