Embodied AI Glossary中文

07Software & Tooling

The software that ties body, control, and perception together: ROS 2, URDF, motion libraries, learning frameworks, and deployment tools. · 232 terms

  1. 7.1Development environment basics11
  2. 7.2Getting started with ROS 223
  3. 7.3Robot models and coordinate frames17
  4. 7.4Communication middleware, in depth23
  5. 7.5Connecting real hardware, real time23
  6. 7.6Kinematics, planning, and control libraries30
  7. 7.7Perception, mapping, and navigation17
  8. 7.8Data logging and visualization10
  9. 7.9Deep learning and robot-learning libraries29
  10. 7.10Training engineering and compute14
  11. 7.11Deployment and inference speedups21
  12. 7.12Industry platforms and ecosystems14

7.1Development environment basics

First set up your machine: programming languages, Linux, code repositories, environment isolation, GPUs, and remote access.

7.2Getting started with ROS 2

With your environment ready, learn robotics software’s ‘glue’: nodes, topics, and other communication patterns, plus how projects are organized.

7.3Robot models and coordinate frames

Once you know ROS, describe a robot with files like URDF, then visualize it with TF and RViz.

7.4Communication middleware, in depth

Digging back into the communication layer: DDS, QoS, zero-copy, and communication libraries beyond ROS.

7.5Connecting real hardware, real time

With communication working, connect real hardware: vendor SDKs, drivers, embedded and real-time systems that get commands to the motors.

7.6Kinematics, planning, and control libraries

Once hardware can send and receive commands, use existing libraries for kinematics, collision-free planning, and optimal control.

7.7Perception, mapping, and navigation

Helping the robot understand its environment: image and point-cloud libraries, calibration, SLAM mapping, and autonomous navigation.

7.8Data logging and visualization

Recording and visualizing sensor and robot state: useful for debugging, and for collecting data to train on later.

7.9Deep learning and robot-learning libraries

With data in hand, start learning: deep learning frameworks, model hubs, then LeRobot and reinforcement learning libraries.

7.10Training engineering and compute

Getting training running, then scaling it up: renting GPUs, tracking experiments, saving memory, multi-GPU clusters, and large-scale RL.

7.11Deployment and inference speedups

Moving a trained policy onto the robot: running it remotely or on-device, sped up with kernel optimization, export, and inference engines.

7.12Industry platforms and ecosystems

Finally, how vendors package all these layers together: NVIDIA Isaac, Chinese robot operating systems, and cloud platforms.

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