Autonomous Resilience

In a crisis, the first hour is everything.

When a wildfire, flood, or earthquake strikes, our centralized emergency systems are often the first to fail. Cell towers go down. 911 is overwhelmed. First-responders are flying blind, and help is often hours or days away—if it comes at all.

Challenge 1: ORIN agent Challenge 2: ACRF Agent

This is a high-ambition research challenge for public safety

The mission is to research a sovereign, autonomous rescue platform designed to function when other systems are offline. It is intended to serve as the “Guardian” of the AmberCore ecosystem, with the goal of providing a 24/7, AI-supported response capability to users, anywhere on Earth, with or without a cell signal.

This platform is a federated AI operating within the AmberCore ecosystem. It consists of two interconnected agents that our Fellows will build:

ORIN (The "Watchtower"): The AI "mind" that sees and predicts a crisis from space.
ACRF (The "Fleet Commander"): The AI "hands" that autonomously dispatches a swarm of drones and vehicles to respond.

We are recruiting the teams to build these agents. These are our "Resilience" R&D Challenges:

Challenge 1 (R&D-ARC1):

The ORIN Agent - "The Digital Watchtower"

Mission: Build a predictive, real-time AI agent that can autonomously identify, track, and triage global crises, transforming raw sensor data into actionable intelligence for the ACRF rescue fleet.

The Problem: When a disaster strikes, information is chaotic. The ORIN agent’s role is to create clarity. It is designed as the digital watchtower that integrates the “big picture” from space (for example, a wildfire’s path) with the “local picture” from a user’s SOS ping, fusing them into a single, actionable assessment for the rescue fleet.

The Technical Challenge: Your challenge is to build the ORIN agent to autonomously ingest, process, and understand multiple high-volume data streams (satellite, geospatial, weather). Its primary output is not just data, but judgment: a prioritized list of verified “crisis events” that the ACRF (Fleet Commander) agent can act on.

Core Objectives (Key Deliverables):

Data Fusion Engine: Develop a model that can ingest and correlate disparate data types in real-time (Satellite Imagery, Geospatial Data, Real-time Feeds, Pendant SOS Pings).
Predictive Modeling: Build and train predictive models for at least two major disaster types (e.g., Wildfire Path Prediction, Flash Flood Plain Analysis) to forecast a threat’s progression.
Autonomous SOS Triage: Design a workflow that, upon receiving a Pendant SOS, automatically queries all other data streams to enrich that signal and autonomously classify its severity and context.
ACRF "Target Package" API: Create a standardized API output (a "Target Package") that contains all the processed intel the ACRF agent needs to plan a mission (coordinates, threat type, hazards, priority).

Provided Resources (Your Toolkit):

The AmberCore Core SDK (with a simulated SOS signal feed).
Curated, real-world training datasets (satellite imagery, terrain data).
Generous access to a high-performance computing cluster for model training.
Mentorship from Caliteco's core architects and subject-matter experts in geospatial intelligence.

We're Looking For (Required Skills):

Machine Learning/AI (Python, TensorFlow/PyTorch)
Geospatial Information Systems (GIS)
Computer Vision (CV) (for satellite image segmentation)
Data Science & Engineering

How Success is Measured:

Accuracy: How accurately do your predictive models forecast the disaster's path?
Speed: What is the mean time-to-triage (from SOS Ping to Target Package generation)?
Scalability: Can your agent handle 1,000 simultaneous SOS signals during a major event?

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Challenge 2 (R&D-ARC2):

The ACRF Agent - "The Fleet Commander"

Mission: Build the autonomous "fleet commander" AI agent that can receive a crisis-response task from the ORIN "Watchtower" and execute it in the physical world by managing a "swarm" of drones and a central "Mothership."

The Problem: The ORIN "Watchtower" has just issued a "Target Package." A human operator would be overwhelmed. The ACRF Agent is designed to address this. It is the real-time logistics brain that receives requests from ORIN and autonomously manages the physical fleet, connecting digital intel to physical action.

The Technical Challenge: This is a problem of multi-agent, real-time, autonomous logistics and swarm robotics. Your agent’s “input” is the Target Package. Its “output” is a set of autonomous command vectors sent to the fleet’s hardware.

Core Objectives (Key Deliverables):

Autonomous Task Triage & Allocation: Ingest multiple, competing Target Package requests from ORIN and autonomously prioritize them and assign the optimal asset (e.g., Drone-Light, Drone-Heavy, Rescue, Medical Supply, Mothership).
Dynamic Multi-Agent Pathfinding (Swarm Logic): Develop a 4D pathfinding algorithm (3D space + time) that can plot the most efficient route, factoring in real-world constraints (terrain, weather, no-fly zones) and ensuring “deconfliction” so drones don’t collide.
Fleet "Health" & Logistics Management: The agent must manage the fleet as a resource. It must monitor battery/fuel, execute autonomous “return-to-base” logic for recharging, and factor in payload limitations.
Mission Execution & Hand-off: Develop the logic for the final “last-mile” maneuver (e.g., payload drop-off) and confirm mission-complete status back to the UAI.

Provided Resources (Your Toolkit):

The AmberCore Core SDK (with a “virtual” ORIN agent feeding you Target Package requests).
A "Digital Twin" Simulation Environment (e.g., AirSim, Gazebo) with a virtual fleet of drones and a Mothership.
Geospatial & Threat Data (3D terrain maps, simulated “threat-zones”).
Mentorship from Caliteco’s advisors in robotics, autonomous systems, and logistics.

We're Looking For (Required Skills):

Robotics & Autonomous Systems (ROS, pathfinding algorithms)
AI/Reinforcement Learning
Operations Research & Logistics
Software Engineering (C++, Python) and experience with physics simulators.

How Success is Measured:

Efficiency: What is the average "time-to-delivery" in the simulation?
Robustness: How many "mission failures" (e.g., drone crashes, battery failures) does your agent have?
Scalability: How does your agent's performance scale when the fleet grows from 10 to 100 drones?

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Your contribution matters

If you are an AI researcher, robotics engineer, or geospatial data scientist who wants to solve one of the most critical public safety challenges of our time, this is your mission.

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