Autonomous retail robot restocking beverages in a convenience store while working alongside a store employee, illustrating coordinated human-robot collaboration for smarter shelf replenishment and store operations.

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Corroborative Task Scheduler for Robots

Store robots only help when they understand the shelf, the shopper, and the team around them. This approach turns live store conditions into coordinated work for people and machines.

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A shelf can look full in the system and empty to the customer.
Automation fails when it ignores the moment.

WHY THIS “CORROBORATIVE TASK SCHEDULER FOR ROBOTS" CHALLENGE?

Deciding what’s next is harder than restocking itself

A multinational manufacturer is exploring multi-task robots with arms and multimodal AI to support convenience-store employees. The first target is restocking drinks, but the hard part is deciding what happens next based on priorities.

Key benefits

The shelf becomes the schedule

Turn store signals into the next best task

Shelf status, customer presence and employee availability help determine what action should happen next.

Let people and robots share the workload

Restocking tasks are coordinated across employees and robots according to store priorities.

Simulate before the aisle gets busy

A digital store model tests robot actions for safety and efficiency before real deployment.

Keep products available without overloading teams

Better task timing helps customers find what they need while reducing repetitive work.

How NTT DATA helps

01

Situation-aware sensing layer

Live store context is captured: shelf status, product availability, customer presence, and employee location.

Smart retail environment using real-time sensing to monitor shelf status, product availability, customer presence and employee location

02

Task scheduling and optimization logic

Scheduling models balance sales priorities, operating cost, store policies, and available human-robot capacity.

Retail operations using intelligent task scheduling to balance sales priorities, operating costs, store policies and human-robot capacity

03

Digital twin validation

Robot actions are simulated inside a virtual store environment to test movement, sequence, safety, and efficiency before deployment.

Digital twin environment simulating robot movement, task sequences, safety and efficiency before real-world deployment

04

Human-robot workflow design

Task logic helps people and robots work together with less friction, instead of automating around employees.

Autonomous inspection robot navigating an industrial manufacturing plant while reading analog gauges and monitoring equipment, enabling safer, continuous asset inspection and predictive maintenance.

05

Industrialization path for store operations

Prototype logic moves into repeatable processes that can support broader store robotics adoption.

Automated conveyor system supporting autonomous logistics workflows and efficient material handling

Busy retail environment illustrating how real-time signals help coordinate robotic support with staff activity and customer flow

Proven impact

Live signals, better timing

Robots start supporting the people already keeping shelves moving, instead of working around them.

Results that matter

Operational outcomes from store robotics

Improve shelf availability

Coordinate restocking so customers are more likely to find the products they want.

Support sales and inventory balance

Use live store data to reduce missed replenishment moments and avoid unnecessary stock pressure.

Reduce employee workload

Shift repetitive checking, arranging and restocking prompts into a coordinated scheduling system.

Make robot deployment safer

Validate actions in a digital store model before robots operate around customers and employees.

Store robotics work where people do

Drive the conversation that turns live store conditions into coordinated action

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