Session 1: Problem Definition

Teams focus on understanding and framing the problem within their chosen challenge area. Guided activities will help participants:

• Explore the operational context and stakeholders
• Identify friction points, constraints, and unmet needs
• Clearly articulate the problem they intend to solve

This session emphasizes diverging before converging, encouraging teams to consider multiple perspectives before settling on a specific problem statement.

Session 2: Solutioning

Teams shift from problem space to solution space. Using structured ideation techniques, participants will:

• Generate and refine solution concepts
• Consider how robotics, autonomy, and geospatial intelligence enable the solution
• Think through feasibility, value proposition, and differentiation

By the end of this session, each team should have a clear concept for a product or service that addresses the defined problem.

Session 3: Synthesis and Pitch Development

The final working session focuses on synthesizing ideas into a coherent narrative. Teams will:

• Clarify the problem–solution fit
• Articulate who the solution is for and why it matters
• Prepare a concise, compelling pitch

The emphasis is on clarity, impact, and storytelling rather than technical depth alone.

Team Pitches and Celebration

At the end of the day, each team will deliver a 5 minute pitch to all summit attendees, presenting their problem, solution, and vision. Following the presentations:

• All participants will vote on the solutions
• One team per category will be recognized as workshop winners

The day will conclude with a happy hour celebration, recognizing the creativity, collaboration, and effort of all teams because the real value of the workshop is the ideas generated and connections made.

Challenge Area 1: Digital Twins, Infrastructure, and Autonomous Systems

BLUF: This challenge area examines how robots and autonomous systems can safely and effectively operate within complex, large scale built environments by leveraging digital twins and high fidelity geospatial context.

Infrastructure environments such as construction sites, transportation networks, utilities, industrial facilities are dynamic, partially structured, and often hazardous. Autonomous systems operating in these settings (e.g., inspection robots, autonomous construction equipment, maintenance vehicles, mobile sensors) require far more than basic navigation. They depend on accurate spatial context, semantic understanding of assets, and continuously updated representations of the environment.

Key challenge dimensions include:

• Robot ready digital twins: Moving from human oriented visualization models to machine interpretable digital twins that autonomous systems can use for planning, perception, and decision making.
• Autonomy across lifecycle phases: Enabling robots to operate during design validation, construction progress tracking, inspection, maintenance, and long term operations; each with different spatial and operational constraints.
• Change detection and situational awareness: Detecting deviations between “as designed,” “as built,” and “as operated” conditions so autonomous systems can adapt safely in near real time.
• Human–robot interaction in infrastructure environments: Ensuring autonomous systems can safely coexist with human workers and the public in active, changing job sites and community settings.

For ideation, participants should focus on:

1. Where robots fail today due to outdated, incomplete, or non actionable spatial models
2. How digital twins could directly inform robotic perception, task planning, and control
3. What infrastructure tasks remain manual or dangerous because autonomy lacks spatial understanding

Challenge Area 2: Environment, Agriculture, Public Safety, & National Security

BLUF: This challenge area focuses on deploying autonomous systems in large scale, unstructured, and high consequence outdoor environments, where geospatial awareness is essential for mission success.

Robots operating in environmental monitoring, agriculture, disaster response, and security contexts must handle vast geographic areas, uncertain terrain, dynamic conditions, and degraded positioning signals. These systems increasingly rely on geo enabled autonomy combining onboard sensing with external spatial intelligence to operate reliably at scale.

Key challenge dimensions include:

• Autonomous sensing and intervention at scale: Using robots (air, ground, surface, subsurface) to monitor environments, perform remediation, manage crops, or respond to emergencies across large areas.
• Resilient navigation and localization: Enabling autonomous systems to function in GNSS‑denied, degraded, or contested environments using alternative spatial cues, maps, resilient PNT alternatives, and cooperative localization.
• Real‑time geospatial feedback loops: Closing the loop between deployed robots, remote sensing, GeoAI analytics, and command systems to continuously improve autonomy.
• Rapid deployment and coordination: Coordinating fleets of autonomous systems during time‑critical public safety or security scenarios.

For ideation, participants should focus on:

1. Where autonomy breaks down when positioning, communications, or maps are unreliable
2. How geospatial intelligence can extend the operational envelope of robots
3. What decisions could be automated or accelerated if robots had better spatial and temporal context

Challenge Area 3: Life Sciences & the Indoor World

BLUF: This challenge area explores autonomous systems operating in indoor, human centric, and safety critical environments, where precision, reliability, and trust are non negotiable.

Indoor environments such as hospitals, laboratories, pharmaceutical manufacturing facilities, warehouses, and homes present a distinct set of robotics challenges. GNSS is unavailable, spaces are crowded and dynamic, and errors can have serious health, safety, or regulatory consequences. Autonomous systems in these settings must demonstrate exceptional spatial accuracy and predictable behavior.

Key challenge dimensions include:

• Indoor localization and mapping for autonomy: Creating and maintaining highly accurate, up to date indoor maps that robots can rely on for navigation and task execution.
• Safe operation around people and sensitive processes: Ensuring robots can navigate tight spaces, avoid dynamic obstacles, and operate safely alongside patients, clinicians, and technicians.
• Task level autonomy in regulated environments: Supporting use cases such as hospital logistics, disinfection, lab automation, and manufacturing while meeting strict compliance and validation requirements.
• System integration and workflow alignment: Embedding autonomous systems into existing indoor operations without disrupting critical workflows.

For ideation, participants should focus on:

1. Which indoor tasks remain manual due to localization, safety, or reliability constraints
2. Where current indoor autonomy solutions struggle to scale or generalize
3. How better spatial understanding could unlock new classes of indoor robotic applications

Pittsburgh Robotics Network Happy Hour

The Pittsburgh Robotics Network invites Summit participants to its monthly PRN Happy Hour—an opportunity to connect, network, and engage with leaders, innovators, and entrepreneurs from across Pittsburgh’s dynamic robotics community.