Tag: Embodied AI
28 articles
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
AI Investigation #93: Robot Simulation Tools - Comprehensive Comparison from Gazebo to Isaac Sim
Simulation tools are an important part of robot R&D, enabling algorithm verification and system debugging in risk-free environments, accelerating iteration.
AI Investigation #92: Robot Motion Control - From Traditional Models to Deep Learning Methods
Robot motion control can be divided into two categories: traditional model-based methods and deep learning-based intelligent control.
AI Investigation #91: Multi-modal Data Annotation Tools - From Label Studio to 3D Point Cloud Labeling
In robot vision and perception model training, high-quality multi-modal data annotation tools are crucial.
AI Investigation #90: Robot Data Collection and Communication Middleware: ROS/ROS2, LCM and Industrial Bus Comparison
Modern robot systems require efficient data collection and communication middleware to connect sensors, controllers and computing units, achieving collaborative perceptio...
AI Investigation #76: When Robots Enter Life - Embodied AI's Deep Impact on Employment and Social Structure
The widespread application of embodied AI is profoundly changing social structure.
AI Investigation #75: From LLM to LBM - Hierarchical Robot Control Architecture Driven by Large Models
The integration of Large Language Models (LLM) with robot real-time control is driving intelligent upgrades in robotics.
AI Investigation #74: Robot Learning Breakthroughs - Meta-Learning and Sim-to-Real Transfer
This article explores fast learning capabilities of embodied AI agents, focusing on meta-learning and few-shot learning methods.
AI Investigation #73: Embodied AI Future Trends - From Technology Integration to Industrial Deployment
In the next decade, embodied AI will undergo paradigm shifts: centered on 'pre-trained world models + online learning', software-hardware collaboration and interdisciplin...
AI Investigation #72: Embodied AI Development Challenges - Data, Hardware, Compute and Commercialization
Embodied AI development faces six core challenges: data scarcity, hardware limitations, training efficiency, cost bottlenecks, standardization and industrial ecosystem...
AI Investigation #71: Embodied AI Case Studies - From ROS to Tesla Optimus in Open Source and Commercial Practice
Typical practices of embodied AI in architecture, capabilities and applications.
AI Investigation #70: Embodied AI Industry Ecology and Development Trends
Embodied AI is leading a new round of technological revolution. The market size is expected to grow from $2.53 billion in 2024 to $8.76 billion in 2033, with a CAGR of 15...
AI Investigation #69: Embodied AI Key Capabilities - Algorithms, Hardware, Simulation and Data
Systematic overview of five key capability dimensions of embodied AI: intelligent algorithms, high-performance hardware, simulation and virtual environments...
AI Investigation #68: Embodied AI Application Landscape - Home, Industry, Healthcare, Transportation and Virtual Interaction
Systematically explore the application prospects and development trends of embodied AI in multiple fields, covering core scenarios including home, industry, healthcare.
AI Investigation #67: Embodied AI Core Technology - Perception, Decision, Learning and Interaction in a Closed Loop
Embodied AI technology can be summarized as a 'perception-decision-control-learning-interaction' closed-loop system.