M08-08 · AI + Robotics & Automation Operations

Professional Practice in Robotics Operations

AI + Robotics & Automation Operations →

Builds on CORE-08's freelance foundations to address the specialized professional practices of robotics operations careers. Covers robot data management (LIDAR, camera, and telemetry data streams, retention policies, privacy considerations for robots with cameras/microphones in homes and hotels), warehouse and logistics operations fundamentals (WMS, order fulfillment, conveyor systems), building operations functions from scratch (playbooks, runbooks, monitoring dashboards, hiring plans), presenting operational data to executives and investors, and the field-to-engineering feedback loops that turn operational experience into product improvement. The extended hours reflect a substantial capstone simulation running concurrent deployments.

65 Hours
9 Learning objectives
Evaluate Bloom's ceiling (?)
6 Competencies

Learning Objectives

Objectives

Depth
  • Create data management policies for robot-generated data — LIDAR scans, camera feeds, telemetry streams, interaction logs — specifying what to collect, storage duration, privacy controls, access permissions, and cost-optimized retention (on-device 24-48h for raw data, cloud 90 days for aggregated telemetry) Create
  • Analyze privacy considerations for robots operating in sensitive environments (homes, hotels, care facilities) — configuring camera/microphone activation rules, local vs. cloud processing, privacy indicators, data deletion procedures, and child/pet safety behaviors Analyze
  • Apply warehouse and logistics operations fundamentals — WMS workflow (order → pick task → robot assignment → execution → status update), order fulfillment processes, conveyor system integration — well enough to manage robot operations within the broader facility context Apply
  • Create an operations function from scratch: deployment playbook, support runbook with tiered escalation, monitoring dashboards, customer reporting templates, spare parts logistics, training program for new hires, and a field-to-engineering feedback loop with structured weekly syncs Create
  • Evaluate fleet-wide operational data to produce executive presentations — throughput impact, labor cost reduction, injury rate improvements, ROI analysis, and budget justification for the next automation investment cycle Evaluate
  • Apply structured field-to-engineering feedback practices — ranking issues by frequency and severity, providing specific data (sensor logs, coordinates, environmental conditions) rather than vague complaints, and tracking feature requests across customer sites Apply
  • Create an operations hiring plan — job descriptions based on actual role requirements, interview protocols, onboarding with deployment shadowing and troubleshooting certification, and training documentation that reduces ramp time for new field engineers Create
  • Analyze how to navigate the tension between startup speed and operational safety, articulating non-negotiable safety requirements to leadership while finding creative ways to accelerate deployment timelines Analyze
  • Evaluate data-driven operational improvements — identifying idle time reduction opportunities (task assignment lag, map staleness, zone imbalance), running simulations (NVIDIA Isaac Sim/Gazebo) for layout changes, and presenting improvement proposals with predicted ROI Evaluate

Levels: Remember · Understand · Apply · Analyze · Evaluate · Create — highest demands most original thinking.

What You'll Master

Robot Data Management

Designing data collection, storage, retention, and privacy policies for robots that generate terabytes of sensor data weekly; balancing diagnostic needs against storage costs and privacy obligations.

Warehouse & Logistics Operations

Understanding the WMS-driven fulfillment process, conveyor and sortation systems, human picker workflows, and how robot operations integrate into the broader operational ecosystem.

Operations Function Design

Building the complete operational infrastructure from zero: playbooks, runbooks, dashboards, reporting, inventory, hiring, training, and feedback loops; creating systems that work without you.

Executive Communication

Translating operational data into business language for VP/Director/C-suite audiences; building ROI analyses that justify automation investment; presenting to investors with operational metrics that demonstrate scalability.

Field-to-Engineering Feedback

Serving as the conduit between customer-facing operations and product engineering; providing structured, data-rich feedback that drives product improvements; tracking the feedback-to-implementation pipeline.

Operational Scaling

Transitioning from doing everything yourself to building systems, hiring, and delegating; recognizing when operational volume requires the next hire, the next process, or the next tool investment.

What You'll Build

Robotics Operations Capstone — Student runs a 6-week capstone simulation managing concurrent robotics operations: 3 active customer sites with ongoing monitoring and support, 1 new deployment (full lifecycle from site survey to go-live), and 1 customer at risk of churn requiring a recovery plan. Deliverables include: complete operations playbook (deployment, support, maintenance, updates), fleet performance dashboards, monthly customer ROI reports, a data management and privacy policy, executive presentation summarizing 90-day operational performance with investment recommendation, field-to-engineering feedback report (top 10 issues with supporting data), and an operations hiring plan for scaling from 1 to 3 field engineers.

Industry Tools, Not Toy Projects

Fleet Management Platform

Freedom Robotics, InOrbit, or Formant for fleet monitoring, telemetry, and operational dashboards.

NVIDIA Isaac Sim / Gazebo

Robotics simulation for pre-deployment testing, layout optimization, and operational improvement validation.

WMS (Manhattan / Blue Yonder)

Warehouse management systems for understanding fulfillment workflows and robot-WMS integration.

CMMS (Fiix / UpKeep)

Maintenance management for tracking work orders, spare parts, and building predictive maintenance datasets.

Google Sheets / Excel

ROI models, fleet tracking, capacity planning, and operational performance analysis.

Power BI / Tableau

Executive dashboards and fleet performance visualization for stakeholder reporting.

Jira / Linear

Engineering feedback tracking, feature request management, and issue pipeline monitoring.

Notion / Confluence

Operations playbooks, knowledge base, and institutional documentation for scaling teams.

CRM (Salesforce / HubSpot)

Customer account management, escalation tracking, and renewal pipeline monitoring.

Claude

AI-assisted documentation, analysis, communication drafting, and operational research.

PagerDuty

Alerting and on-call management for fleet operations monitoring and incident response.

Prerequisites

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