القائمة
Zeroth Bot - 3D-printed open-source humanoid robot platform for sim-to-real and RL research. Affordable entry point for humanoid robotics.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
استنادا إلى تصنيف SOC المهني
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/plurigrid/asi --skill zeroth-botيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
Latched Hyperprior Hypothesis (LHH) — a sustained vascular contraction engages the smooth-muscle latch-bridge, durably freezing a circuit as a committed hyperprior isolated from global updating; unlatches when its prediction resolves. Use when modeling durable commitments, trauma/PTSD as cemented priors, latch spirals, or the cross-substrate latch (mechanical/bioelectric/immune/sheaf).
Synaptic substrate of biological active inference — long-term potentiation/depression (LTP/LTD) writes priors into synaptic weights = the learning landscape (Deep CANALs). A held vascular latch annealed long enough crystallizes into a neuron prior. Use when modeling consolidation, neuron priors, the inference→learning landscape write-path, or commit-to-disk of a held prediction.
| name | zeroth-bot |
| description | Zeroth Bot - 3D-printed open-source humanoid robot platform for sim-to-real and RL research. Affordable entry point for humanoid robotics. |
| version | 1.0.0 |
| category | robotics-platform |
| author | K-Scale Labs |
| source | kscalelabs/zeroth-bot |
| license | MIT |
| trit | -1 |
| trit_label | MINUS |
| color | #8CC136 |
| verified | false |
| featured | false |
Trit: -1 (MINUS - specification/verification) Color: #8CC136 (Lime Green) URI: skill://zeroth-bot#8CC136
Zeroth Bot (Z-Bot) is a 3D-printed open-source humanoid robot platform designed for sim-to-real research and RL experimentation. An affordable entry point for humanoid robotics.
┌────────────────────────────────────────────────────────────────┐
│ ZEROTH BOT (Z-BOT) │
├────────────────────────────────────────────────────────────────┤
│ │
│ Height: ~40cm │
│ Weight: ~2kg │
│ DOF: 12 joints │
│ │
│ Frame: 3D printed (PLA/PETG) │
│ Actuators: Servo motors │
│ Cost: ~$500 BOM │
│ │
│ Ideal for: │
│ ├── Learning sim-to-real transfer │
│ ├── Testing RL policies at low cost │
│ ├── Educational robotics │
│ └── Rapid prototyping │
│ │
└────────────────────────────────────────────────────────────────┘
| Component | Quantity | Notes |
|---|---|---|
| Servo motors | 12 | Standard hobby servos |
| 3D printed parts | Full set | STL files provided |
| MCU | 1 | ESP32 or Teensy |
| IMU | 1 | MPU6050 or similar |
| Power | 1 | 2S-3S LiPo |
from ksim.robots.zbot import ZBotConfig
from ksim import PPOTask
class ZBotWalking(PPOTask):
robot = ZBotConfig(
model_path="zbot.mjcf",
servo_config={
: ,
: ,
: ,
}
)
training_config = {
: ,
: ,
}
zeroth-bot (-1) ⊗ kos-firmware (+1) ⊗ mujoco-scenes (0) = 0 ✓
kbot-humanoid (-1): Larger flagship humanoidksim-rl (-1): RL training librarykos-firmware (+1): Firmware (kos-zbot variant)urdf2mjcf (-1): Model conversionZ-Bot has a web-based control interface:
// From zbot-web-frontend
import { ZBotController } from '@kscale/zbot-web';
const controller = new ZBotController({
endpoint: 'ws://zbot.local:8080',
});
await controller.connect();
await controller.setJointPositions({
hip_pitch_l: 0.5,
knee_l: -0.3,
});
@misc{zerothbot2024,
title={Zeroth Bot: 3D-Printed Open-Source Humanoid},
author={K-Scale Labs},
year={2024},
url={https://github.com/kscalelabs/zeroth-bot}
}
This skill connects to Software Design for Flexibility (Hanson & Sussman, 2021):
Concepts: DSL, wrapper, pattern-directed, embedding
zeroth-bot (−) + SDF.Ch2 (−) + [balancer] (−) = 0
Skill Trit: -1 (MINUS - verification)
DSLs embed domain knowledge. This skill defines domain-specific operations.