Task Outline: Developing an LLM-Based Behavior Generation Engine for a Robot

Introduction

With the arrival of the SDNU freshman class of 2023, the need for more advanced and intelligent robotic behavior systems has become apparent. Previously, an intelligent dialogue software for robots was developed based on large language models (LLMs). However, with the rapid advancement of embodied intelligence, this software is now seen as less capable. To address this, the goal is to develop an LLM-based behavior generation engine that enables robots to understand and react to human commands more effectively.

The RoboCodeX framework provides a foundation for generating multimodal code for robotic behavior synthesis. By building on this framework, we aim to create an intelligent engine that can be deployed on robots like Nao. This engine will enhance the robot's ability to comprehend natural language instructions and execute corresponding behaviors.

Procedure

Stage 1: Constructing an Inference Engine

Objectives:

1. Research and Selection:

   • Understand and compare several mainstream inference engines such as Transformer, VLLM, Tensor-RT, DeepSpeed, and Text Generation Inference.
   • Choose the most suitable inference engine based on criteria like performance, compatibility, and ease of deployment.

2. Deployment:

   • Deploy the selected inference engine in a suitable environment.
   • Document the deployment process, including system specifications (OS, GPU model, etc.).

3. Performance Testing:

   • Measure and document the throughput (number of inferences per second) of the deployed inference engine. It is recommended to use higher accuracy of reasoning

   • Analyze the results and identify any bottlenecks.

Deliverables:

• A detailed report on the research and selection process of the inference engine.
• Documentation of the deployment process, including system specifications.
• Performance testing results and analysis.

Stage 2: Optimizing the Inference Engine

Objectives:

1. Performance Tuning:

   • Optimize the chosen inference engine to maximize its throughput.
   • Experiment with different settings and configurations to enhance performance.

2. Benchmarking:

   • Conduct rigorous benchmarking to compare performance before and after optimization.
   • Document the methods and tools used for optimization.

3. Analysis and Documentation:

   • Analyze the results of the optimizations.
   • Provide detailed documentation on the optimization steps, configurations used, and the impact on performance.

Deliverables:

• A comprehensive guide on the optimization process, including tools and methods used.
• Comparative benchmarking results (before and after optimization).
• Detailed analysis of the optimization impact.

Stage 3: Deploying OpenVLA and Developing API Interfaces for Robots

Objectives:

1. Deploying OpenVLA:

   • Set up and deploy the OpenVLA framework in a suitable environment.
   • (Document the deployment process, including system specifications (OS, GPU model, etc.).)

2. Developing API Interfaces:

   • Develop API interfaces for robots like Nao using the deployed OpenVLA framework.
   • Ensure the APIs enable the robots to execute behaviors generated by the LLM-based behavior generation engine.

3. Integration and Testing:

   • Integrate the developed APIs with the existing robotic platforms.
   • Conduct thorough testing to ensure that the robots can understand and react to human commands effectively using the OpenVLA framework.

Deliverables:

• Documentation of the deployment process for OpenVLA, including system specifications.
• Developed API interfaces for Nao or other robots, with detailed documentation.
• Test results and analysis, demonstrating the effectiveness of the integrated system.

General Notes

• Environment Setup: Clearly document the environment setup for each stage, specifying the operating system, GPU model, and any other relevant system details.
• Tools and Frameworks: Utilize appropriate tools and frameworks for deployment, optimization, and fine-tuning (e.g., Python, PyTorch, TensorFlow, NVIDIA CUDA, etc.).
• Documentation: Ensure all processes are thoroughly documented to facilitate reproducibility and understanding.