aether-robotics 3.4.2

Autonomous multi-agent robotics system with DRL-First Hybrid FDIR

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AETHER v3

Adaptive Embodied Task Hierarchy for Executable Robotics

DRL-First Hybrid FDIR · Multi-Agent · Self-Correcting

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AETHER is a multi-agent robotics framework that detects, diagnoses, and recovers from hardware faults in real time using a Deep Reinforcement Learning-first approach. It auto-discovers whatever hardware is connected — webcam, GPIO motors, flight controller — builds a capability manifest, and constructs a complete autonomy stack from planning through execution. A PPO neural network serves as the primary fault detector, backed by rule-based safety checks and temporal validation, achieving perfect detection and recovery rates on real hardware across thousands of operational steps.

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Quick Start

# 1. Install
pip install aether-robotics

# 2. Calibrate your hardware
aether --calibrate

# 3. Run in agent mode
aether --mode agent

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What It Does

DRL-First Hybrid FDIR

Traditional fault detection relies on hand-written threshold rules that break when conditions change. AETHER inverts this: a PPO neural network (15-dim observation → 64 → 64 → 8 fault classes) is the primary detector, with rule-based checks as a safety backup. The network self-bootstraps from scratch using the rule detector as a teacher, then surpasses it through online learning.

The detection pipeline runs every step:

1. PPO Network infers fault class probabilities from the 15-dimensional observation vector (battery, IMU, temperature, obstacle distances, mission progress)
2. Temporal Validation filters transient spikes to prevent false positives
3. Confidence Arbitration (threshold τ=0.12) decides whether to alert
4. Critical Bypass (σ≥0.80) escalates severe faults directly, skipping temporal filtering

Fault classes: SENSOR_FAILURE, ACTUATOR_DEGRADATION, POWER_CRITICAL, THERMAL_ANOMALY, IMU_DRIFT, INTERMITTENT_FAULT, SAFE_MODE

Auto-Configuration

AETHER discovers its own hardware at startup — no config files required. ToolDiscovery probes for cameras (OpenCV, picamera2), GPIO pins (RPi.GPIO, gpiozero), flight controllers (MAVLink over serial/USB), I2C sensors, network interfaces, and AI models (YOLOv8, Claude API). The result is a capability manifest that drives everything downstream: ToolBuilder constructs only the tools that will work, NavigationEngine selects the correct autonomy level, and LLMPlanner avoids planning with unavailable hardware.

Three capability levels adapt automatically:

Level	Hardware	Capabilities
1	Camera only	Visual scan, object detection, scene description
2	Camera + GPIO motors	Level 1 + navigation, obstacle avoidance, color tracking
3	Camera + MAVLink FC	Level 2 + takeoff, landing, waypoint navigation, RTL

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Benchmark Results

Platform	SFRI	Detection Rate	Recovery Rate	FPR
Laptop (simulation, 5,000 runs)	60.07	74.7%	80–90%	2–5%
Raspberry Pi (real hardware, 6,023 steps)	69.99	100%	100%	0.0%
SpeedyBee Drone	TBD	TBD	TBD	TBD

SFRI (Stability Fault Recovery Index) = 35×DR + 25×(1 − MTTR/max_steps) + 10×RR − 30×FPR Range: 0–70. Higher is better.

Real hardware outperforms simulation because the physical system encounters genuine sensor noise that the PPO network learns to distinguish from actual faults, while simulation injects idealized fault signatures that can mislead the temporal validator.

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Supported Hardware

Platform	Details
:computer: Laptop + USB webcam	Level 1 autonomy — development & testing
:strawberry: Raspberry Pi + Pi Camera	Level 1 — headless visual perception
:strawberry: Raspberry Pi + GPIO motors	Level 2 — ground vehicle navigation
:helicopter: Raspberry Pi + SpeedyBee FC	Level 3 — autonomous drone flight

Additional: I2C sensors, serial UART, ultrasonic rangefinders, IMU, temperature probes, LiDAR, battery monitoring.

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Architecture

┌──────────────────────────────────────────────────────────────────┐
│                        AETHER v3 PIPELINE                        │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────────┐   │
│  │ ToolDiscovery │───▶│ ToolBuilder  │───▶│  ToolRegistry    │   │
│  │ probe hw/sw   │    │ build tools  │    │  register all    │   │
│  └──────────────┘    └──────────────┘    └────────┬─────────┘   │
│         │                                          │             │
│         ▼                                          ▼             │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────────┐   │
│  │  Calibration  │    │  GoalParser  │───▶│   LLMPlanner     │   │
│  │  Wizard       │    │  NL → struct │    │  Claude / kw     │   │
│  └──────────────┘    └──────────────┘    └────────┬─────────┘   │
│                                                    │             │
│                                                    ▼             │
│  ┌───────────────────────────────────────────────────────────┐   │
│  │                   EXECUTION LOOP                          │   │
│  │  ┌─────────────┐  ┌──────────────┐  ┌────────────────┐   │   │
│  │  │ Navigation  │  │  Perception  │  │  Correction    │   │   │
│  │  │ Engine      │  │  Agent       │  │  Agent         │   │   │
│  │  │ L1/L2/L3    │  │  15-dim obs  │  │  verify steps  │   │   │
│  │  └──────┬──────┘  └──────┬───────┘  └────────────────┘   │   │
│  │         │                │                                │   │
│  │         ▼                ▼                                │   │
│  │  ┌─────────────────────────────────────────────────────┐  │   │
│  │  │              FAULT AGENT (DRL-First)                │  │   │
│  │  │  PPO Network ──▶ Temporal Validation ──▶ Response   │  │   │
│  │  │  (15→64→64→8)    confidence filter      adaptation  │  │   │
│  │  │       ▲               ▲                     │       │  │   │
│  │  │       │               │                     ▼       │  │   │
│  │  │  Rule Backup    Memory Agent         Recovery       │  │   │
│  │  │  (safety net)   (experience)         Action         │  │   │
│  │  └─────────────────────────────────────────────────────┘  │   │
│  └───────────────────────────────────────────────────────────┘   │
│                              │                                   │
│                              ▼                                   │
│  ┌──────────────────────────────────────────────────────────┐    │
│  │  MetricsTracker  ──▶  Visualizer  ──▶  logs/plots/      │    │
│  │  SFRI · MTTD · MTTR · DR · RR · FPR · reward curves    │    │
│  └──────────────────────────────────────────────────────────┘    │
└──────────────────────────────────────────────────────────────────┘

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Calibration

The CalibrationWizard runs when new hardware is detected that hasn't been profiled before. It walks through 7 phases:

1. Component Identification — asks robot type (ground, aerial, arm, aquatic, custom)
2. Motor Mapping — tests each channel at low power, records what physically moved
3. Camera-Assisted Verification — uses optical flow to confirm motor-to-function assignments
4. Environment Mapping — drives a grid pattern to build a 100x100 occupancy grid
5. Safety Limits — sets per-component speed/angle/power limits
6. Capability Generation — saves a JSON robot profile to configs/
7. Action Generation — builds the correct action set for the detected robot type

Run with --auto-calibrate for headless (no interactive prompts) calibration using camera feedback only.

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CLI Reference

Flag	Default	Description
--mode {sim,agent,realworld,server}	sim	Operating mode
--task TEXT	"navigate to target"	Natural language task objective
--robot {rover_v1,drone_v1}	rover_v1	Robot platform configuration
--scenario {simple,obstacles,imu_fault,battery,compound,fault_heavy}	simple	Simulation scenario
--faults {disabled,enabled,heavy}	disabled	Fault injection level
--max-steps N	300	Maximum steps per episode
--seed N	42	Random seed
--port N	8080	HTTP server port (server mode)
--render	off	ASCII render each simulation step
--plots	off	Generate matplotlib plots after run
--verbose	off	Debug logging
--continuous	off	Run indefinitely in realworld mode
--no-learning	off	Disable online PPO learning (fixed weights)
--calibrate	off	Run hardware calibration wizard
--recalibrate	off	Force re-calibration over existing profile
--auto-calibrate	off	Camera-only auto calibration (no prompts)
--auto-install	off	Install missing packages without asking
--auto-update	off	Update without asking
--no-install	off	Skip install prompts
--no-update	off	Skip update check

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Citation

If you use AETHER in your research, please cite:

@software{aether2026,
  title     = {AETHER: Adaptive Embodied Task Hierarchy for Executable Robotics},
  author    = {Paatur, Chahel},
  year      = {2026},
  version   = {3.0},
  url       = {https://github.com/ChahelPaatur/AETHER},
  note      = {DRL-First Hybrid FDIR with multi-agent auto-configuration},
}