Hexapod agent powered by Gemma4:e4b

Gemma 4 Challenge: Write about Gemma 4 Submission Gemma 4 挑战赛：关于 Gemma 4 参赛作品的介绍

This is a submission for the Gemma 4 Challenge: Build With Gemma 4. 这是我为“Gemma 4 挑战赛：基于 Gemma 4 构建”所提交的作品。

Gemma4:e4b Hexapod Robot Project Gemma4:e4b 六足机器人项目

What I Built 项目简介

I built an AI-powered Hexapod Robot capable of autonomous navigation and dynamic gait adjustment. The project solves the complexity of coordinating 18 servos to maintain stability across uneven terrain, creating an experience where the robot can ‘reason’ about its movement based on sensor feedback rather than relying on hard-coded patterns. 我构建了一款由人工智能驱动的六足机器人，具备自主导航和动态步态调整能力。该项目解决了协调 18 个舵机以在不平坦地形上保持平衡的复杂难题，使机器人能够根据传感器反馈对移动进行“推理”，而非仅仅依赖硬编码的预设模式。

Code 代码仓库

Iron-Hermes Agent Client Code Main Repository: bradwilson331/iron-hermes
TCP Control Module: hexapod_tcp.rs
Video Handling Module: hexapod_video.rs
Server Code (Unmodified) Hardware Server: Freenove Big Hexapod Robot Kit for Raspberry Pi
Iron-Hermes 智能体客户端代码主仓库：bradwilson331/iron-hermes
TCP 控制模块：hexapod_tcp.rs
视频处理模块：hexapod_video.rs
服务器代码（未修改）：Freenove 树莓派大型六足机器人套件

How I Used Gemma 4 Gemma 4 的应用方式

I utilized the gemma4:e4b model. I chose the e4b model because it fits locally on my Mac Mini, yet provides the sophisticated spatial reasoning and logic precision required to translate high-level commands (e.g., “navigate to the red object”) into explicit, coordinate-based gait adjustments and parameter generation. 我使用了 gemma4:e4b 模型。选择 e4b 模型是因为它可以在我的 Mac Mini 上本地运行，同时又能提供复杂的空间推理和逻辑精度，从而将高级指令（例如“导航到红色物体”）转化为明确的、基于坐标的步态调整和参数生成。

System Architecture & Communication 系统架构与通信

The hexapod server operates across 2 dedicated ports: 六足机器人服务器通过两个专用端口运行：

Port 5000: Command Control (TCP)
Port 8000: Video Transmission
端口 5000：指令控制 (TCP)
端口 8000：视频传输

Operational Workflow 操作流程

1. The Initial Command (Action) 1. 初始指令（动作）

Trigger: The user issues a command instructing the agent to move forward and stop when it detects an object in close proximity.
Decision: The Large Language Model (LLM) determines it is safe and time to initiate movement.
Tool Called: hexapod_tcp.rs
Execution: The agent invokes the TCP tool with the exact string protocol required by the robot’s onboard hardware to initiate forward movement (e.g., CMD_MOVE#FORWARD#SPEED50). The robot physically begins walking.
触发： 用户发出指令，要求智能体向前移动，并在检测到附近有物体时停止。
决策： 大语言模型 (LLM) 判断当前环境安全，并决定开始移动。
调用工具： hexapod_tcp.rs
执行： 智能体调用 TCP 工具，发送机器人板载硬件所需的精确字符串协议以启动前进动作（例如 CMD_MOVE#FORWARD#SPEED50）。机器人随即开始行走。

2. The Sensory Polling Loop (Perception) 2. 感知轮询循环（感知）

Because LLMs do not possess a continuous, constant “stream” of consciousness, the agent actively polls its environment at regular intervals while the robot is in motion by repeatedly executing its sensory tools: 由于大语言模型不具备连续、恒定的“意识流”，智能体在机器人移动过程中会通过重复执行感知工具，定期主动轮询环境：

Imaging via hexapod_video.rs: The agent executes this tool to capture the latest frame from the onboard camera (protected via an ENV_LOCK). The model analyzes the image matrix to determine whether a visual obstruction exists, such as a wall or a drop-off.
Sonar via hexapod_tcp.rs: To acquire precise, real-time distance data, the agent dispatches a telemetry request over the open TCP socket (e.g., GET_SONAR_DIST). The tool awaits the hardware return payload, receives the integer distance data (e.g., 15cm), and feeds that string back into the LLM context window for subsequent action evaluation.
通过 hexapod_video.rs 进行图像采集： 智能体执行此工具以捕获板载摄像头的最新帧（通过 ENV_LOCK 保护）。模型分析图像矩阵，以确定是否存在视觉障碍物，例如墙壁或悬崖。
通过 hexapod_tcp.rs 进行声纳探测： 为了获取精确的实时距离数据，智能体通过开放的 TCP 套接字发送遥测请求（例如 GET_SONAR_DIST）。工具等待硬件返回数据包，接收整数距离数据（例如 15cm），并将该字符串反馈回 LLM 上下文窗口，用于后续的动作评估。