Gleam Lab · Blog Archive
Blog Page 4
Technical exploration and engineering notes, 655 articles in total.
AI Research #121: DeepSeek-OCR Research Directions
Frontier approaches and engineering implementation for DeepSeek-OCR (2025, including 3B parameter direction).
AI Research #119: DeepSeek-OCR PyTorch FlashAttn 2.7.3 Inference and Deployment
Comprehensive guide for DeepSeek-OCR local/private deployment based on Python 3.12, PyTorch 2.6.0, Transformers 4.46.3 and FlashAttention 2.7.3.
AI Research #120: DeepSeek-OCR from 0 to 1 - Getting Started and Engineering Essentials
Complete getting started path and engineering essentials for DeepSeek-OCR (as of 2025), covering environment setup (Python/PyTorch 2.x, Transformers 4.
AI Research #118: Embodied AI Mobile-ALOHA - Mobile Base + Dual-Arm Collaboration
Mobile-ALOHA: An open-source mobile manipulation solution combining mobile chassis and dual-arm collaboration.
AI Research #117: Tesla FSD Vision Analysis - Multi-camera Fusion and Occupancy Network
Detailed analysis of Tesla's 3D rendering (Occupancy Network): multi-camera spatiotemporal fusion → voxel occupancy → bird's eye coordinates.
AI Research #116: Tesla HW3.0 vs HW4.0 - Camera Resolution, Compute and Perception Upgrade
Comprehensive comparison of Tesla HW3.0 and HW4.0 hardware: camera resolution upgraded from 1.2MP to 5MP with better HDR/night vision
AI Investigation #108: Complete Robot Model Training Pipeline - From Pre-training to Reinforcement Learning and Human Feedback
Full robot training pipeline: pre-training, fine-tuning (LoRA), reinforcement learning, imitation learning, and human feedback for safe autonomous decision-making.
AI Investigation #107: RL and Robot Training Data Format Analysis
Constructed in state-action-reward sequence form, supporting spatiotemporal understanding of models like Transformers.
AI Investigation #106: Robot Learning Data Collection Tools and Methods - Sensors, APIs, Teleoperation and Simulation
Core data collection methods and application scenarios, covering over ten methods from manual entry, sensor collection, web crawlers, API calls, log collection.
AI Investigation #105: Robot Learning Data Collection - From Demonstration Videos to State-Action Pairs
Data collection is a critical step in robot learning development, covering demonstration video collection, trajectory recording, state-action pair generation...
AI Investigation #104: From Model Training to Robot Deployment - ONNX, TensorRT and Triton
AI model deployment optimization guide: ONNX conversion, TensorRT/OpenVINO inference engines, quantization (FP16/INT8), and real-time robotics applications.
AI Investigation #103: Embodied AI Technology Landscape
Comprehensive overview of embodied AI tech stack: hardware (GPU, sensors, actuators), software (ROS, simulation), and algorithms (deep learning, RL, VLA models).
AI Investigation #102: Intelligent Robotic Arms, Autonomous Driving and Humanoid Robots - Imitation Learning, Reinforcement Learning and Multimodal Fusion Trends
Different types of robots have huge differences in structure, tasks and control methods, so AI algorithm adaptation strategies also need to be tailored.
AI Investigation #101: Modern AI Methods - VLA, RT-1, RT-2 and Diffusion Models for Robot Control
Modern AI robot control methods are undergoing a major transition from reinforcement learning and imitation learning to multimodal agents driven by large models.
AI Investigation #100: Modern AI Methods - Reinforcement Learning, Imitation Learning and Transformers for Robot Control
Modern AI methods for robot control cover Reinforcement Learning (RL), Imitation Learning (IL), and Transformer-based large model methods.
AI Investigation #99: Sensor Fusion Technology - Camera, LiDAR, IMU and Radar Fusion
Sensor Fusion is a core technology in autonomous driving, robotics and smart security.
AI Investigation #98: Visual SLAM - ORB-SLAM, RTAB-Map and VINS-Fusion
Visual SLAM is a technology that achieves autonomous positioning and environment mapping without relying on LiDAR, using only cameras.
AI Investigation #97: SLAM Algorithm Comparison and Application Scenarios
Multi-sensor fusion and SLAM are core technologies for robot perception and navigation.
AI Investigation #96: Robot Scenario Testing - From Extreme Environments to Real-time Simulation
Complete guide to robot scenario testing, covering three dimensions: environment testing, load testing, and anomaly testing.
AI Investigation #95: Robot Scenario Testing - From Extreme Environment Simulation to Automated Fault Injection
Camera Instant Frame Loss: 5-100ms frame drop LiDAR Noise Surge: Random noise 5-20% IMU Data Jump: 1-3x normal values