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What is david-?

satoshi88818/david- — explained in plain English

Analysis updated 2026-05-18

0Audience · researcherComplexity · 5/5Setup · hard

In one sentence

A software architecture for an autonomous humanoid robot, coordinating vision, world modeling, social prediction, and balance control between sensors and movement.

Mindmap

mindmap
  root((David))
    What It Does
      Humanoid robot architecture
      Reasoning and control layers
    Modules
      Vision Language Action model
      World Model
      Social Grid
      Dreamer loop
      Diagnostic Reasoner
    Coordination
      Planner Node at 10 Hz
    Audience
      Researchers

Code map

Detail Auto

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What do people build with it?

USE CASE 1

Study how a Vision-Language-Action model can turn sensor input into robot movement using diffusion sampling.

USE CASE 2

Learn how a Social Grid predicts nearby people's movements to avoid the hallway dance problem.

How does it compare?

satoshi88818/david-0verflowme/alarm-clock0xhassaan/nn-from-scratch
Stars00
LanguageCSSPython
Last pushed2022-10-03
MaintenanceDormant
Setup difficultyhardeasymoderate
Complexity5/52/54/5
Audienceresearchervibe coderdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1day+

Full README was truncated, hardware and dependency requirements aren't fully shown.

So what is it?

David is a software architecture for an autonomous humanoid robot, a robot shaped like a person that can move and interact in human environments without direct human control. The codebase provides all the reasoning and control layers that sit between raw sensor readings and physical movement. The system is organised into cooperating modules. A Vision-Language-Action model translates what the robot sees and hears into sequences of physical movements using diffusion sampling, generating many possible motion paths and selecting the safest, most useful ones. A World Model stores a constantly updated picture of the robot's surroundings, using a lock-free memory technique so reading the latest state never delays the motor control thread. A Social Grid predicts how nearby people will move using Level-K reasoning: rather than assuming a person walks in a straight line, it models how they would react to the robot changing course, preventing the "hallway dance" where both keep stepping into each other's way. A Dreamer loop filters every planned trajectory using mathematical tools called a Control Barrier Function and a Control Lyapunov Function to guarantee the robot stays balanced and makes progress toward its goal. A Diagnostic Reasoner estimates payload weight and floor friction from joint sensor readings via an Unscented Kalman Filter, a statistical technique for tracking hidden quantities from indirect measurements. All modules are coordinated by a Planner Node running at 10 Hz, ten decision cycles per second. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
Explain how the Dreamer loop uses Control Barrier and Lyapunov Functions to keep the robot balanced.
Prompt 2
Walk me through how the Planner Node coordinates all modules at 10 Hz.
Prompt 3
Summarize how the Diagnostic Reasoner estimates payload weight using an Unscented Kalman Filter.

Frequently asked questions

What is david-?

A software architecture for an autonomous humanoid robot, coordinating vision, world modeling, social prediction, and balance control between sensors and movement.

How hard is david- to set up?

Setup difficulty is rated hard, with roughly 1day+ to a first successful run.

Who is david- for?

Mainly researcher.

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