Agibot has deployed its G2 humanoid robots in an active production environment at Longcheer Technology, a Shanghai-based consumer electronics manufacturer. The rollout marks one of the more concrete demonstrations of humanoid robots operating within a live industrial workflow, moving the technology beyond controlled pilots into continuous factory-floor use.

The deployment comes months after Agibot announced the production of its 10,000th humanoid robot in March, a milestone the company described as a turning point in the industrialization of embodied AI.

What the Robots Are Doing

G2 units are stationed at multimedia-integrated testing stations, where they perform precision loading and unloading of devices into testing fixtures. The task demands millimeter-level placement accuracy, consistent cycle timing, and the ability to sort finished and defective units without interruption.

Agibot reports throughput of up to 310 units per hour, with individual cycle times of approximately 19 to 20 seconds per operation and a success rate exceeding 99% in continuous use. Production line integration was completed within 36 hours, and each shift produces approximately 3,000 units. The system has logged over 140 hours of cumulative continuous operation, with downtime losses below 4%.

The robots use multi-modal sensing – combining visual perception and spatial awareness – to identify objects and execute task sequences without custom tooling. The platform supports mixed-model production, meaning it can handle different device configurations on the same line, reducing changeover time.

The Underlying Architecture

Agibot describes its approach as a full-stack ecosystem for embodied intelligence, integrating robot hardware, AI models, and large-scale data infrastructure into a single system designed for continuous learning. Rather than executing fixed instructions, the robots are built to adapt to task and environment variations over time through a combination of simulation-based validation, reinforcement learning, and on-device inference.

“This project shows that embodied AI is no longer experimental. It is a practical, production-ready capability that can operate reliably in real industrial environments and deliver measurable economic value,” said Maoqing Yao, Partner, Senior Vice President, and President of the Embodied Business Unit at Agibot.

Scale and Next Steps

Agibot plans to expand the deployment to 100 robots by Q3 2026 and has identified automotive, semiconductors, and energy as the next target sectors. The company is also developing Genie Sim 3.0, a simulation platform designed to accelerate training of new robot behaviors before physical deployment.

The broader implication of the Longcheer deployment is not the throughput figures alone, but what they suggest about deployment speed. A 36-hour integration timeline and no custom tooling requirement lower the barrier for manufacturers evaluating humanoid robots against conventional fixed automation. Whether those numbers hold across more varied factory environments – with different layouts, device types, and production rhythms – will determine how transferable the model is at scale.

Accenture has invested in General Robotics, a startup building a unified AI intelligence platform for industrial robots, through its Accenture Ventures arm. The two companies will also partner to help manufacturers, logistics operators, and other asset-intensive industries deploy autonomous robotic systems at scale. Financial terms were not disclosed.

The deal reflects a wider strategic push by Accenture to move beyond software consulting and into the physical infrastructure of AI-driven automation.

The Problem GRID Is Designed to Solve

Most factories operate robots from multiple manufacturers, each running its own software stack, programming language, and integration requirements. Scaling automation across a multi-vendor fleet is expensive and slow, and the cost has historically limited full deployment to only the largest industrial operators.

General Robotics addresses this with GRID, a platform that sits above the hardware layer and connects robots from more than 40 manufacturers – including FANUC, Flexiv, Ghost Robotics, and Galaxea – under a single orchestration framework. Rather than programming each machine individually, GRID offers modular, reusable AI skills deployable across different hardware through cloud-based orchestration, simulation-based training, and full data sovereignty for enterprise customers.

“While robotics hardware and AI models advance at a rapid pace, real-world impact is constrained by the lack of a unified intelligence infrastructure,” said Ashish Kapoor, CEO and co-founder of General Robotics. Kapoor previously served as general manager of autonomous systems and robotics research at Microsoft, where he created AirSim, a widely used open-source simulator for training autonomous vehicles and drones.

Accenture’s Physical AI Strategy

The investment extends an infrastructure position Accenture has been building for over a year. The company launched its Physical AI Orchestrator in October 2025, a system that uses NVIDIA Omniverse libraries and the NVIDIA Mega Blueprint to coordinate robotic and autonomous systems in industrial settings. GRID integrates NVIDIA Isaac Sim, allowing manufacturers to train robotic AI skills in digital twins before deploying them on physical hardware – a capability that aligns directly with Accenture’s existing toolchain.

Where Accenture’s Physical AI Orchestrator handles coordination at the facility level, GRID handles robot-level AI – the skills, perception, and decision-making that individual machines need to perform complex tasks autonomously. Together, the two layers form a more complete stack for enterprise robotics deployment.

Prior investments in Sanctuary AI and a partnership with Schaeffler for industrial humanoid robots in automotive manufacturing point to a consistent thesis: Accenture is positioning itself as the primary integrator for physical AI at the enterprise level.

Scale and Market Context

“Piloting robotic systems takes too long, is expensive, and often not scalable and repeatable across a network of facilities,” said Prasad Satyavolu, Accenture’s global lead for manufacturing and operations. The stated goal of the partnership is to compress that deployment cycle by delivering an enterprise-grade robotics intelligence and orchestration layer that clients can apply across multiple facilities.

The physical AI market is projected to grow from roughly $1.5 billion in 2026 to more than $15 billion by 2032. A Deloitte survey found that 58% of global business leaders are already using some form of physical AI, though scaled deployment remains concentrated in automotive, electronics, and logistics. General Robotics remains an early-stage company without publicly reported revenue figures, and the broader challenge – persuading manufacturers to adopt an independent orchestration layer over proprietary vendor platforms – will require demonstrated performance on working factory floors, not just in simulation.

Grab unveiled an AI-powered delivery robot called Carri at GrabX 2026, the company’s annual product event held in Jakarta this month. The announcement is part of a broader push by the Singapore-based super app to embed physical automation into a platform that has long depended entirely on gig workers.

Anthony Tan, Grab’s CEO and co-founder, said the company is building an Intelligence Layer – AI infrastructure fueled by real-world, real-time signals – that sits underneath every feature and innovation in the app. That layer now extends into hardware.

Carri and the Indoor Delivery Problem

Tan said delivery partners currently lose around 10% of their earning time navigating large malls or waiting for customers to come down from office buildings. Carri is designed to absorb that idle time by handling restaurant retrieval and handoff, freeing drivers to stay on the road.

The robot is built for both indoor and outdoor environments, equipped with LIDAR sensors and cameras to navigate crowds and avoid obstacles. It features secure storage compartments that open only for the specific user assigned to a given order.

Carri is still in the development and testing phase, and the pricing or cost model for deployment has not yet been determined. Tan framed the move as a natural extension of the platform’s AI capabilities into the physical world. “We are moving into hardware to improve the messy physical parts of the job that software alone cannot fix,” he said.

13 New Features Across Three User Groups

GrabX 2026 introduced 13 AI-powered features designed around three core pillars: local life, effortless travel, and business empowerment.

For consumers, Grab introduced Group Ride for shared fares, GrabMore for multi-merchant orders under a single delivery fee, and a Grab AI Assistant that handles food, shopping, and bookings. GrabMaps and a Cash Loan product round out the consumer-facing additions.

For travelers, Grab unveiled GrabStays for hotel bookings, Discover by Grab for AI-curated dining recommendations, and GrabPay for Travel to enable cross-border QR payments across Southeast Asia.

For merchants and drivers, Grab announced a Virtual Store Manager using CCTV hardware for AI-powered monitoring, a Cloud Printer to automate order handling, and Tap to Pay to turn smartphones into contactless payment terminals. A Driver AI Assistant provides hands-free route and earnings guidance.

Monetization and the Road Ahead

Tan said Grab is also planning to extend its intelligence layer into autonomous vehicles and CCTV cameras, signaling that hardware will become a structural component of the business rather than a peripheral experiment.

On the revenue side, merchant hardware tools including cloud printers and virtual store management are set to move from free trials to a subscription model. A recent Barclays analysis estimated that widespread use of robots and drones in food delivery could reduce per-order costs to as little as $1 – a threshold that, if reached, would reshape the unit economics of every major delivery platform in the region.

A mobile irrigation robot developed by researchers at the University of California, Riverside is challenging one of agriculture’s most persistent assumptions: that crops in the same field require the same amount of water.

By mapping soil moisture at the level of individual trees, the system reveals significant variation even between neighboring plants, suggesting that conventional irrigation methods may be systematically inefficient.

The findings point to a broader shift in agricultural robotics, where mobile sensing systems are replacing static infrastructure to deliver more granular, data-driven decisions.

From Field Averages to Tree-Level Precision

Traditional irrigation relies on fixed sensors and uniform watering schedules, operating on the assumption that conditions are relatively consistent across a field. The robot developed at UCR takes a different approach, scanning soil conditions continuously as it moves through orchards.

In field trials across citrus groves in California, the system detected sharp differences in water availability between adjacent trees, despite identical irrigation inputs. These variations were linked to differences in soil composition, where finer soils retained water more effectively than sandier patches.

The robot measures electrical conductivity in the soil – a proxy for moisture – and combines those readings with calibration data from a limited number of ground sensors. The result is a detailed moisture map that identifies both under-watered and over-watered areas.

This level of resolution allows irrigation to be adjusted at a much finer scale, turning what has traditionally been a field-wide estimate into a localized decision.

Reducing Waste and Managing Risk

The implications extend beyond water conservation. Overwatering can damage crops by depriving roots of oxygen and increasing susceptibility to disease, while also washing fertilizers deeper into the soil, where they can no longer be absorbed.

By identifying these imbalances, the system enables growers to maintain soil moisture within a narrower, optimal range. In testing, the model achieved high accuracy with relatively few calibration points, suggesting that widespread deployment may not require dense sensor networks.

This efficiency is significant in an industry where the cost of installing and maintaining sensors can limit adoption of precision agriculture technologies.

The approach also aligns with broader pressures facing agriculture, particularly in water-constrained regions. As drought conditions intensify, growers are increasingly forced to either reduce production or find ways to use water more efficiently.

Robotics Expands Beyond Automation

Unlike many agricultural robots focused on harvesting or crop monitoring, this system highlights a different role for robotics: acting as a mobile data layer that enhances decision-making rather than directly performing physical tasks.

The platform used in the study is capable of autonomous navigation, although it was manually operated during trials. Future versions are expected to operate independently, covering larger areas and integrating more closely with irrigation systems.

Several challenges remain before commercial deployment, including adapting the system to different crops, soil types, and environmental conditions. The relationship between surface measurements and deeper soil moisture also requires further refinement.

The development reflects a broader trend in robotics toward combining mobility with sensing and AI-driven analysis. By moving through environments rather than relying on fixed points, robots can capture variability that static systems miss.

In agriculture, where small differences in soil conditions can have large impacts on yield and resource use, that shift may prove particularly consequential.

If validated at scale, tree-level irrigation mapping could redefine how farms manage water – not as a uniform input, but as a variable resource tailored to each plant.