AI Masterclass in Robotics

Abstract

The masterclass introduced TCS’s vision of Physical AI – the fusion of digital AI models with physical robotic assets to create production‑ready, resilient solutions for inspection, material handling, and safety. The session traced the evolution from deterministic AI and industrial robotics to generative AI, collaborative robots, and today’s agent‑based orchestration. After outlining why India is uniquely positioned to adopt AI‑native factories, the speakers showcased real‑world use‑cases (humanoid Echo, quadruped Poochee, AGVs, drones) and presented a live deployment example in an agri‑tech warehouse. The latter half of the session turned into a guided, hands‑on lab where participants logged into TCS’s low‑code “AI Orchestrator” platform, configured workflows, and exercised the physical assets at their tables.

Detailed Summary

Speaker	Main Points
Naresh Mehta (opening)	• Introduced the agenda: a brief 15‑minute conceptual overview followed by a 75‑minute hands‑on lab. • Described the physical assets that would be on stage – miniature robotic arms, Automated Guided Vehicles (AGVs), and a quadruped robot.
Laksh Parthasarthy	• Presented Echo, TCS’s first humanoid, trained on TCS culture and deployed on a few customer sites for tasks that require human‑like interaction. • Presented Poochee, a quadruped robot already deployed at three customers for inspection and safety monitoring. • Emphasised that the assets shown are real implementations, not just slides.

2. Evolution of AI & Robotics

Deterministic Era – Early AI and industrial robotics were rule‑driven, trained on historical data, and limited to repetitive, pre‑programmed actions.
Generative AI – Introduced as a catalyst that augments human work, simplifying daily tasks and enabling new capabilities.
Collaborative Robots (Cobots) – Highlighted the human‑plus‑robot operating model that began to bring augmentation into the physical workspace.
Agent‑KI – Described as the next layer that orchestrates multiple AI agents and physical assets, stitching together complex workflows.
Physical AI – Defined as the convergence point where digital AI models are directly linked to physical assets, enabling “software‑defined physical intelligence.”

3. Why India & the “Physical AI” Opportunity

Insight	Explanation
Supply‑Chain Resilience	In the West, AI is applied to reduce brittleness in global supply chains (geopolitical risks, tariffs, etc.).
India’s Unique Landscape	India faces distinct challenges: a need for sovereign capabilities, poly‑sourcing, and the chance to leap from legacy OT systems to AI‑native factories and industrial corridors.
Capex & Consumption Model	Large market for Robotics‑as‑a‑Service (RaaS) and diffusion of robotics across industries.
Workforce Dynamics	Unlike the West, India does not face a major skill‑gap; instead, AI can amplify the existing workforce, enabling higher productivity.
Social Impact	Physical AI can bridge the “last‑mile” gap for public services (education, healthcare) by automating intermediate processes.

4. Manifestations of Physical AI

Humanoids (e.g., Echo) – Pilot projects focusing on human‑like interaction, dexterous hands, and the ability to handle payloads of 3–7 lb. Benefit: 24/7 availability.
Autonomous Vehicles (AGVs/AMRs) – Integration of V2X communication for logistics and port operations.
Quadruped Robots (Poochee) – Mature deployments for inspection, especially in hazardous environments.
Drone + Quadruped Orchestration – Combining aerial imagery with ground‑level sensing for richer analytics.

5. Real‑World Deployment Case Study (Agri‑Tech Customer)

Component	Details
Customer Context	Large food‑processing warehouse with frequent oil spills, gas leaks, ammonia vapors.
Problem	Manual inspections every 4 h took > 90 min each and posed safety risks.
Solution Architecture	1️⃣ Selected quadruped robot for inspection. 2️⃣ Integrated LiDAR sensing and GPU‑accelerated preprocessing (NVIDIA stack). 3️⃣ Built, deployed, and continuously refined AI models for anomaly detection.
Results	• Safety incidents reduced by ~90 %. • Operational downtime cut by ~30 %. • Deployments now live at 3 European sites and 1 site in China (30 quadruped units operating).
Key Message	The use‑case demonstrates how a full end‑to‑end AI pipeline (hardware, data, model, deployment) can deliver measurable, production‑grade outcomes.

6. Transition to Hands‑On Lab

Logistics – Participants were assigned tables containing two devices each (e.g., an AGV and a quadruped, or a robotic arm).
Mentor System – Each device type had dedicated mentors (e.g., “Puppy‑pie mentors” for quadrupeds) who would circulate, answer questions, and troubleshoot.
Team Formation – Up to three participants per table; empty seats were filled as needed.

6.1. Platform Introduction – “AI Orchestrator”

Feature	Description
Low‑Code/No‑Code Workflow	A catalog of AI resources (IoT connectivity, data pipelines, models) that can be plug‑and‑play into physical assets.
Physical AI Template	Pre‑built collections of templates matched to each device (AGV, quadruped, robotic arm).
Virtual Desktop Access	Participants log into a TCS‑hosted virtual desktop, then into the orchestrator portal.
Configuration Steps	1️⃣ Log in → 2️⃣ Select appropriate device template → 3️⃣ Adjust parameters/tune → 4️⃣ Publish workflow → 5️⃣ Test on the physical asset.
Testing Areas	Dedicated testing zones for each device type (e.g., an AGV track, a quadruped arena, a stationary robotic arm table).

6.2. Demo by “Pratip”

Walked through the login flow, highlighted the catalog of templates, and explained how the system deploys the selected workflow onto the chosen hardware.
Emphasised that participants would edit and publish their own configurations under mentor guidance.

6.3. Lab Execution

Participants paired each device with a laptop (one‑to‑one mapping).
Mentors assisted with:
- Logging into virtual desktops.
- Selecting the correct template for their device.
- Modifying parameters (e.g., sensor thresholds, model versions).
- Publishing and observing the robot execute the defined task.
The facilitator (Laksh) repeatedly reminded attendees that mistakes are welcome; mentors would help troubleshoot in real time.

7. Closing & “Games Begin”

The session concluded with an invitation for all mentors to move to their tables and start the interactive portion.
Participants were encouraged to experiment, iterate, and experience how a physical AI blueprint can be turned into a working robot without writing code.

Key Takeaways

Physical AI is the convergence of digital AI models with tangible robotic assets, enabling software‑defined intelligence in the physical world.
India offers a strategic advantage for AI‑native factories because of its supply‑chain dynamics, large market for Robotics‑as‑a‑Service, and a workforce ready for augmentation rather than replacement.
TCS’s humanoid Echo and quadruped Poochee are production‑grade examples that illustrate real‑world deployments and 24/7 operational capability.
A real‑world case study (agri‑tech warehouse) showed that AI‑driven quadruped inspection can cut safety incidents by ~90 % and downtime by ~30 %, proving quantifiable ROI.
The AI Orchestrator platform provides a low‑code, template‑driven workflow that lets users configure, deploy, and test AI models on physical devices without deep programming expertise.
Hands‑on labs demonstrated that teams can rapidly prototype AI‑enabled robotics, reinforcing the masterclass’s promise of replicable blueprints for scaling.
Mentor‑driven guidance is crucial for successful adoption; the session emphasized iterative learning and troubleshooting in situ.
The masterclass highlighted that physical AI can also address social challenges (last‑mile delivery of public services) by automating intermediate processes.
Overall, the session combined strategic vision, technical depth, and practical experience, equipping participants with both the why and the how of deploying production‑ready AI robotics at scale.

See Also:

India AI Impact Summit 2026

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