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XiaomiRobotics

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README

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Running

🔧 Quick Start:

Python:
import requests

API_KEY = "your_key_here" # Get from https://nworobotics.cloud

response = requests.post(
"https://nwo.capital/webapp/api-robotics.php?action=inference",
headers={"X-API-Key": API_KEY},
json={
"instruction": "Pick up the red box",
"image": "base64_or_url",
"iot_sensors": {
"gps": {"lat": 40.7128, "lng": -74.0060},
"temperature": 23.5,
"lidar": [2.4, 3.1, 1.8]
},
"include_iot_context": True
}
)
print(response.json()["actions"])
cURL:
curl -X POST "https://nwo.capital/webapp/api-robotics.php?action=inference"
-H "X-API-Key: your_key"
-d '{"instruction":"Pick up box","iot_sensors":{"gps":{"lat":40.71,"lng":-74.01}}}'
Returns:
{
"actions": [{"joint_0": 0.5, "gripper": 0.8}],
"confidence": 0.94,
"iot_context": {"temperature_note": "Optimal"}
}

🚀 Major Update: Task Planner (Layer 3) & Learning System (Layer 4) Now Live!

We've expanded NWO Robotics API from a simple inference endpoint to a complete 4-layer intelligent robotics platform:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📋 LAYER 3: TASK PLANNER
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Break high-level instructions into executable subtasks automatically!

Example:
User says: "Clean the room"
API generates 5 subtasks with dependencies:

1. Scan room for trash and objects
2. Pick up trash items (depends on #1)
3. Move chairs to proper positions (depends on #1)
4. Organize displaced items (depends on #1)
5. Vacuum floor (depends on #2, #3, #4)

Code Example:

import requests

# Create a task plan
response = requests.post(
    "https://nwo.capital/webapp/api-task-planner.php?action=plan",
    headers={"X-API-Key": "your_key"},
    json={"instruction": "Clean the room"}
)

plan = response.json()
print(f"Plan ID: {plan['plan_id']}")
print(f"Total steps: {plan['total_steps']}")

# Execute the plan
requests.post(
    "https://nwo.capital/webapp/api-task-planner.php?action=execute",
    headers={"X-API-Key": "your_key"},
    json={"plan_id": plan['plan_id']}
)

# Get next task
task = requests.post(
    "https://nwo.capital/webapp/api-task-planner.php?action=next",
    headers={"X-API-Key": "your_key"},
    json={"plan_id": plan['plan_id']}
).json()

print(f"Execute: {task['task']['instruction']}")

# Mark complete when done
requests.post(
    "https://nwo.capital/webapp/api-task-planner.php?action=complete_task",
    headers={"X-API-Key": "your_key"},
    json={
        "queue_id": task['task']['queue_id'],
        "success": True,
        "result": {"actions_executed": 4}
    }
)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🧬 LAYER 4: LEARNING SYSTEM
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Your robot learns from successes/failures and auto-optimizes parameters!

Real Example:
Robot fails to pick up glass 3 times (grip too strong)
→ System suggests: "Decrease grip_force by 0.15"
→ After adjustment: 90% success rate! 🎯

Code Example:

# Get learned parameters for specific task + object
response = requests.post(
    "https://nwo.capital/webapp/api-learning.php?action=get_parameters",
    headers={"X-API-Key": "your_key"},
    json={
        "task_type": "pick_object",
        "object_type": "glass"  # vs "metal", "box", etc.
    }
)

params = response.json()
print(f"Optimized grip_force: {params['parameters']['grip_force']}")
print(f"Success rate: {params['learning_stats']['success_rate']:.0%}")
# Returns: grip_force = 0.45 (gentle for glass)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
💬 NEW: Chat-to-Agent from Dashboard!
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We also added a Chat button directly in the Developer Dashboard:

1. Go to: https://nwo.capital/webapp/api-key.php
2. Click on any agent (e.g., "Warehouse PickBot-01")
3. Click the Chat button next to "Online" status
4. Opens Agent Command Terminal modal
5. Type natural language: "Pick up the red box"
6. See real-time response with confidence & actions!
   Perfect for testing commands before integrating into your app!

NWO Robotics API WHITEPAPER A Production-Grade Platform for Vision-Language-Action Robotics

Abstract
This paper presents the NWO Robotics API, a comprehensive middleware platform that transforms standalone Vision-Language-Action (VLA) models into production-ready robotic systems. Built upon the Xiaomi-Robotics-0 foundation, our four-layer architecture introduces Task Planning, Learning Systems, IoT Edge Networks, and Enterprise Operations to address the deployment gap between research models and industrial robotics applications. We demonstrate measurable improvements in task completion rates (156% increase), adaptive parameter optimization (90%+ success after learning), and system reliability (99.9% uptime) through empirical evaluation across diverse deployment scenarios.
This paper also incorporates a technical appendix detailing the architectural integration of Layer 3 (Task Planning) and Layer 4 (Learning Systems) with the Xiaomi-Robotics-0 Vision-Language- Action model. While Xiaomi-Robotics-0 provides state-of-the-art visual-motor reflex capabilities, it operates as a single-step reactive system without memory, planning, or adaptation mechanisms. We demonstrate how a modular API architecture can augment base VLA models with high-level task decomposition and parameter optimization through empirical interaction logging, resulting in measurable performance improvements in robotic task execution.
Taken together, the material supports the conclusion that after the addition of planning, learning, IoT context, deployment infrastructure, edge support, analytics, security, and middleware orchestration, the principal missing component for fully autonomous agent robots is compact on-device reasoning: a small language model sufficiently efficient for mobile devices or robot brains, able to provide persistent deliberation, local goal management, and low-latency autonomy without cloud dependence.

Preprint on Researchgate:
https://www.researchgate.net/publication/401902987_NWO_Robotics_API_WHITEPAPER_A_Production-Grade_Platform_for_Vision-Language-Action_Robotics

🚀 Major Update: Intelligent Multi-Model Routing Now Live!

We've been hard at work building the next generation of robotics middleware on top of Xiaomi-Robotics-0. Introducing NWO Robotics API v2.0 with intelligent Language Model routing!

What's New:

🧠 Smart Task Classification - Our router automatically detects what type of task your robot needs:

• OCR/Document reading → DeepSeek-OCR-2B
• Manipulation → Xiaomi Robotics-0
• Navigation → Xiaomi Robotics-0
• General chat → Qwen-VL
⚡ Intelligent Model Selection - Scores models on:

• Task capability match (40%)
• Historical success rate (30%)
• Latency performance (15%)
• Cost efficiency (5%)
• Priority ranking (10%)
🔄 Automatic Fallback Chain - If your primary model fails, instantly falls back to the next best option. Zero downtime.

📊 Developer Dashboard - Configure unlimited LMs per agent:

• Add your own API keys (OpenAI, Anthropic, DeepSeek, etc.)
• Set priority rankings
• View performance analytics
• Track costs per model
🔌 New API Endpoints:
POST /api-model-manager.php?action=add_model
POST /api-model-manager.php?action=preview_routing
Live Demo: https://nwo.capital/webapp/nwo-robotics.html

Technical Paper: https://www.researchgate.net/publication/401902987_NWO_Robotics_API_WHITEPAPER

The future of robotics isn't a single model—it's intelligent orchestration of the right model for the right task. Give it a try! 🤖

🚀 NWO Robotics API v2.0 is LIVE!

We've built a complete API platform on top of Xiaomi-Robotics-0 with two deployment options:

⚡ Edge API (workers.dev)

• 200+ global edge locations
• <50ms latency worldwide
• Zero cold starts
• Perfect for production apps & global users

🖥️ Standard API (nwo.capital)

• Full feature set (Task Planner, Learning System)
• Database-heavy operations
• Best for development & complex workflows
The difference? Edge = speed for simple inference. Standard = power for complex robotics tasks.

Developers can choose based on their needs — same underlying intelligence, different optimization.

Complete API Documentation now updated on: nworobotics.cloud!

Endpoints

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