My name is Ciprian Pater, and I’m reaching out on behalf of PUBLICAE (formerly a student firm at UiA Nyskaping Incubator) to introduce you to NWO Robotics Cloud (nworobotics.cloud) - a comprehensive production-grade API platform we’ve built that extends and enhances the capabilities of the groundbreaking Xiaomi-Robotics-0 model. While Xiaomi-Robotics-0 represents a remarkable achievement in Vision-Language-Action modeling, we’ve identified several critical gaps between a research-grade model and a production-ready robotics platform. Our API addresses these gaps while showcasing the full potential of VLA architecture.
(Attaching some screenshots below for UX reference).
Technical whitepaper at https://www.researchgate.net/publication/401902987_NWO_Robotics_API_WHITEPAPER
NWO Robotics CLI COMMAND GROUPS
Install instantly via pip and start in seconds:
pip install nwo-robotics
Quick Start: nwo auth login → Enter your API key from: nworobotics.cloud → nwo robot “pick up the box”
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• nwo auth - Login/logout with API key
• nwo robot - Send commands, health checks, learn params
• nwo models - List models, preview routing decisions
• nwo swarm - Create swarms, add agents
• nwo iot - Send commands with sensor data
• nwo tasks - Task planning and progress tracking
• nwo learning - Access learning system
• nwo safety - Enable real-time safety monitoring
• nwo templates - Create reusable task templates
• nwo config - Manage CLI configuration etc:
NWO ROBOTICS API v2.0 - BREAKTHROUGH CAPABILITIES
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FEATURE | TECHNICAL DESCRIPTION
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Model Router | Semantic classification + 35% latency
| reduction through intelligent LM selection
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Task Planner | DAG decomposition with topological
| sorting + checkpoint recovery
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Learning System | Vector database + collaborative filtering
| for parameter optimization
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IoT Fusion | Kalman-filtered multi-modal sensor
| streams with sub-10cm accuracy
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Enterprise API | SHA-256 auth, JWT sessions, multi-tenant
| isolation
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Edge Deployment | 200+ locations, Anycast routing, <50ms
| latency, 99.99% SLA
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Model Registry | Real-time p50/p95/p99 metrics + A/B testing
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Robot Control | RESTful endpoints with collision detection
| + <10ms emergency stop
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INTELLIGENT MODEL ROUTER (v2.0)
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Our multi-model routing system analyzes natural language instructions
in real-time using semantic classification algorithms, automatically
selecting the optimal language model for each specific task type.
For OCR tasks, the router selects DeepSeek-OCR-2B with 97% accuracy;
for manipulation tasks, it routes to Xiaomi-Robotics-0. This
intelligent selection reduces inference latency by 35% while
improving task success rates through model specialization.
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TASK PLANNER (Layer 3 Architecture)
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The Task Planner decomposes high-level natural language instructions
into executable subtasks using dependency graph analysis and
topological sorting. When a user requests “Clean the warehouse,”
the system generates a directed acyclic graph of subtasks
(navigate→identify→grasp→transport→place) with estimated durations
and parallel execution paths. This hierarchical planning reduces
complex mission failure rates by implementing checkpoint recovery
at each subtask boundary.
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LEARNING SYSTEM (Layer 4 - Continuous Improvement)
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Our parameter optimization engine maintains a vector database of
task execution outcomes, using collaborative filtering algorithms
to recommend optimal grip forces, approach velocities, and grasp strategies based on historical performance data.
For fragile object manipulation, the system has learned that 0.28N grip force with
12cm/s approach velocity yields 94% success rates across 127 similar
tasks, automatically adjusting robot parameters without human
intervention.
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IOT SENSOR FUSION (Layer 2 - Environmental Context)
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The API integrates multi-modal sensor streams (GPS coordinates,
LiDAR point clouds, IMU orientation, temperature/humidity readings)
into the inference pipeline through Kalman-filtered sensor fusion.
This environmental awareness enables context-aware decision making -
for example, automatically reducing grip force when temperature
sensors detect a hot object, or adjusting navigation paths based
on real-time LiDAR obstacle detection with sub-10cm accuracy.
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ENTERPRISE API INFRASTRUCTURE
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We’ve implemented a complete enterprise API layer including X-API-Key
authentication with SHA-256 hashing, JWT token-based session
management, per-organization rate limiting with token bucket
algorithms, and comprehensive audit logging. The system supports
multi-tenant deployment with complete data isolation between
organizations, enabling commercial deployment scenarios that raw
model weights cannot address.
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EDGE DEPLOYMENT (Global Low-Latency)
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Our Cloudflare Worker deployment distributes inference across 200+
global edge locations using Anycast routing, achieving <50ms response
times from anywhere in the world through intelligent geo-routing.
The serverless architecture eliminates cold start latency entirely
while providing automatic DDoS protection and 99.99% uptime SLA -
critical capabilities for production robotics deployments that
require sub-100ms control loop response times.
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MODEL REGISTRY & PERFORMANCE ANALYTICS
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The Model Registry maintains real-time performance metrics including
per-model success rates, p50/p95/p99 latency percentiles, and
cost-per-inference calculations across different hardware
configurations. This telemetry enables data-driven model selection
and automatic A/B testing of model versions, ensuring optimal
performance as your Xiaomi-Robotics-0 model evolves.
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ROBOT CONTROL API
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We provide RESTful endpoints for real-time robot state querying
(joint angles, gripper position, battery telemetry) and action
execution with safety interlocks. The action execution pipeline
includes collision detection through bounding box overlap
calculations, emergency stop capabilities with <10ms latency, and
execution confirmation through sensor feedback loops - essential
safety features absent from the base model inference API.
MULTI-AGENT COORDINATION
Enable multiple robots to collaborate on complex tasks. Master
agents break down objectives and distribute work to worker agents
with shared memory and handoff zones.
→ Swarm intelligence, task delegation, conflict resolution
FEW-SHOT LEARNING
Robots learn new tasks from just 3-5 demonstrations instead of
programming. Skills adapt to user preferences and improve
continuously from execution feedback.
→ Learn from demonstrations, skill composition, personalisation.
ADVANCED PERCEPTION
Multi-modal sensor fusion (camera, depth, LiDAR, thermal) with
6DOF pose estimation. Detect humans, recognize gestures, predict
motion, and calculate optimal grasp points.
→ 3D scene understanding, human detection, gesture recognition
SAFETY LAYER
Continuous safety validation with 50ms checks. Force/torque
limits, human proximity detection, collision prediction,
configurable safety zones, and full audit logging for compliance.
→ Real-time monitoring, emergency stop, collision prediction
GESTURE CONTROL
Real-time hand gesture recognition for intuitive robot control.
Wave to pause/stop, point to direct attention, draw paths for
navigation. Works from 0.5-3 meters with 95%+ accuracy.
→ Wave to stop, point to indicate location
VOICE WAKE WORD
Always-listening voice activation with custom wake words.
Natural language command parsing with intent extraction. Supports
multiple languages and voice profiles for personalised interactions.
→ “Hey Robot, [command]”
PROGRESS UPDATES
Real-time task progress reporting with time estimation.
Subscribable WebSocket streams for live updates. Milestone
notifications when tasks reach defined checkpoints.
→ “Task 60% complete, 2 minutes remaining”
FAILURE RECOVERY
Intelligent error recovery with strategy adaptation. If grasp
fails, automatically try different angles, grip forces, or
approaches. Escalates to human operator only after exhausting
recovery options.
→ Auto-retry with different angles/strategies
TASK TEMPLATES
Pre-configured task sequences for common workflows. Schedule-based
activation with variable substitution. Templates can be nested,
parameterized, and shared across robot fleets.
→ “Morning routine”, “Closing procedures”
PHYSICS-AWARE PLANNING
Motion planning with real-world physics simulation. Detects
impossible trajectories, unstable grasps, and collision risks
before execution. Integrates with MuJoCo and Isaac Sim.
→ Simulate before execute, avoid physics violations
REAL-TIME SAFETY
Runtime safety monitoring with microsecond latency. Dynamically
adjusts robot speed based on proximity to humans. Emergency stop
with guaranteed response time under 10ms.
→ Continuous monitoring, dynamic speed adjustment
SEMANTIC NAVIGATION
Navigate using natural language landmarks instead of coordinates.
Understand spatial relationships (“next to the table”, "behind
the sofa"). Dynamic path recalculation when obstacles appear.
Thank you in advance for your consideration and feedback.
Sincere Regards
Ciprian Pater
PUBLICAE / NWO Robotics
+4797521288