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Terraform for Humanoid Robotics Fleet Management on AWS

Provision humanoid robotics infrastructure with Terraform: fleet management, OTA updates, simulation clusters, logging, maps, and robotics APIs.

LLuca Berton1 min read

Humanoid and service robotics went from research demo to commercial pilot in 2025–2026. Companies operating fleets of humanoid robots in warehouses, retail, and hospitality need a cloud control plane: fleet inventory, OTA firmware, log capture, simulation, and high-definition maps. Terraform makes that control plane reproducible.

This guide shows how to provision a humanoid-robotics fleet backend on AWS.

Backend Components

#
ComponentAWS service
Fleet registryDynamoDB
Robotics APIsAPI Gateway + Lambda
SimulationEC2 GPU spot fleets, AWS Batch
Maps / SLAM tilesS3 + CloudFront
Log ingestionKinesis Firehose → S3
OTA updatesIoT Jobs
ObservabilityCloudWatch + OpenSearch

Fleet Registry

#
resource "aws_dynamodb_table" "robots" {
  name         = "robots"
  billing_mode = "PAY_PER_REQUEST"
  hash_key     = "robot_id"
 
  attribute {
    name = "robot_id"
    type = "S"
  }
 
  attribute {
    name = "site"
    type = "S"
  }
 
  global_secondary_index {
    name            = "by-site"
    hash_key        = "site"
    projection_type = "ALL"
  }
 
  point_in_time_recovery { enabled = true }
}

Robotics Control API

#
resource "aws_apigatewayv2_api" "robots" {
  name          = "robots-api"
  protocol_type = "HTTP"
}
 
resource "aws_lambda_function" "dispatch" {
  function_name = "robot-dispatch"
  role          = aws_iam_role.dispatch.arn
  package_type  = "Image"
  image_uri     = "${aws_ecr_repository.dispatch.repository_url}:${var.dispatch_tag}"
  timeout       = 15
  memory_size   = 1024
 
  environment {
    variables = {
      ROBOT_TABLE = aws_dynamodb_table.robots.name
      IOT_ENDPOINT = data.aws_iot_endpoint.data.endpoint_address
    }
  }
}

OTA Firmware via IoT Jobs

#
resource "aws_iot_job" "humanoid_firmware_v8" {
  job_id   = "humanoid-firmware-v8"
  targets  = [aws_iot_thing_group.humanoid_fleet.arn]
 
  document = jsonencode({
    operation = "ota_update"
    url       = "s3://${aws_s3_bucket.firmware.bucket}/humanoid/v8.bin"
    sha256    = var.firmware_sha256
    size      = var.firmware_size
    rollback  = "v7"
  })
 
  job_executions_rollout_config {
    maximum_per_minute = 10
    exponential_rate {
      base_rate_per_minute    = 5
      increment_factor        = 2
      rate_increase_criteria {
        number_of_succeeded_things = 50
      }
    }
  }
 
  abort_config {
    criteria_list {
      action          = "CANCEL"
      failure_type    = "FAILED"
      min_number_of_executed_things = 20
      threshold_percentage          = 10
    }
  }
}

GPU Simulation Cluster (Isaac Sim / Gazebo)

#
resource "aws_batch_compute_environment" "sim" {
  compute_environment_name = "humanoid-sim"
  type                     = "MANAGED"
  service_role             = aws_iam_role.batch.arn
 
  compute_resources {
    type                = "SPOT"
    allocation_strategy = "SPOT_CAPACITY_OPTIMIZED"
    bid_percentage      = 60
    instance_type       = ["g5.4xlarge", "g5.8xlarge"]
    min_vcpus           = 0
    desired_vcpus       = 0
    max_vcpus           = 512
    subnets             = var.private_subnet_ids
    security_group_ids  = [aws_security_group.batch.id]
    instance_role       = aws_iam_instance_profile.batch.arn
  }
}
 
resource "aws_batch_job_queue" "sim" {
  name     = "humanoid-sim"
  state    = "ENABLED"
  priority = 10
 
  compute_environment_order {
    order               = 1
    compute_environment = aws_batch_compute_environment.sim.arn
  }
}

HD Maps / SLAM Tile Distribution

#
resource "aws_s3_bucket" "maps" {
  bucket = "acme-humanoid-maps"
}
 
resource "aws_cloudfront_distribution" "maps" {
  enabled             = true
  default_root_object = "index.json"
 
  origin {
    domain_name              = aws_s3_bucket.maps.bucket_regional_domain_name
    origin_id                = "maps-origin"
    origin_access_control_id = aws_cloudfront_origin_access_control.maps.id
  }
 
  default_cache_behavior {
    target_origin_id       = "maps-origin"
    viewer_protocol_policy = "redirect-to-https"
    allowed_methods        = ["GET", "HEAD"]
    cached_methods         = ["GET", "HEAD"]
    cache_policy_id        = data.aws_cloudfront_cache_policy.optimized.id
  }
 
  restrictions {
    geo_restriction { restriction_type = "none" }
  }
 
  viewer_certificate {
    cloudfront_default_certificate = true
  }
}

Best Practices

#
  • Stage OTA jobs: thing-group canary → 10% → 100% with abort thresholds.
  • Replay incidents from cold storage by copying robot logs from S3 into a sim Batch job.
  • Encrypt firmware artifacts with KMS and verify SHA-256 in the OTA document.
  • Per-site Terraform workspaces so you can isolate maps and policies per region.
#
#Terraform#Robotics#AWS#IoT#Simulation

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