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The launch combines a unified, action-oriented world model with Ambient Capture Engine 2.0, three commercial solutions and a new industry benchmark, connecting high-density data with real-world robotic operations.

SHANGHAI, CHINA – Media OutReach Newswire – 19 July 2026 – ACE ROBOTICS today unveiled Kairos 3.1, its latest action-oriented world model, alongside Ambient Capture Engine 2.0 and three commercial solutions spanning instant retail, hospitality and outdoor service scenarios.

The announcements were made at a forum ACE ROBOTICS organized during the 2026 World Artificial Intelligence Conference, convened around the theme of advancing physical AI from “understanding” to “execution.” The event brought together economists, technology executives and embodied AI researchers to examine how general-purpose world models can move from laboratory research to industrial deployment.

The forum also marked the launch of PHYSICAL IQ, a unified benchmark for embodied physical intelligence. It was jointly initiated by the Shanghai Artificial Intelligence Association, the Shenzhen Loop Area Institute and the East China branch of the China Academy of Information and Communications Technology, with participation from more than 20 universities and industry partners.

Thomas J. Sargent, Nobel laureate in Economic Sciences, discussed the limitations of current intelligent systems in rare and previously unseen situations.

A world model built for the physical world

“In the digital world, a model error may result in a flawed image or paragraph. In the physical world, an incorrect action can have real consequences,” said Wang Xiaogang, Chairman of ACE ROBOTICS. “Kairos 3.1 is built around a first-principles approach to embodied world models, helping robots act more reliably in complex and uncertain environments, and accelerating the arrival of physical AI’s Kairos moment.”

Kairos 3.1 is designed as a natively unified model that integrates generative, physical and cognitive intelligence within a single architecture, rather than combining separately developed capabilities.

Built on a hybrid Transformer architecture with a shared mixed-attention mechanism, it brings visual observations, language instructions, force and tactile signals, and policy trajectories into a unified latent space.

This supports an “understand, reason, execute and reflect” loop. The model can break down long-horizon tasks, simulate physical cause and effect across multiple scenarios, rank candidate actions, execute the selected strategy and evaluate the outcome for further adjustment.

At the core of its spatial understanding is ACE-BRAIN-0.5. ACE ROBOTICS said it has achieved state-of-the-art results across 12 public evaluations covering spatial understanding, navigation, manipulation and task-progress assessment, among publicly reported models as of July 2026.

In a household laundry scenario, for example, a robot can identify spatial relationships between objects, divide a task into more than a dozen steps and verify each stage in real time. If a failure occurs, it can identify the affected step and restart from that point rather than repeating the entire task.

For physical generation and reasoning, Kairos-HomeWorld supports whole-home scene generation and object interaction. It is built on 300,000 residential floor plans, 5,000 simulated home environments and 8,700 3D assets covering six categories of physical properties, designed to reflect common residential layouts in China.

The model can simulate multiple action trajectories in parallel and rank them based on predicted success and execution cost before sending instructions to a physical robot.

In internal testing, the Kairos 3.1 8B model achieved an inference latency of 125 milliseconds on the NVIDIA Jetson Thor platform at BF16 precision. Its in-house KairosRT computing engine supports real-time, on-device inference.

Kairos 3.1 also incorporates self-reflective iteration. When an action fails, the robot can evaluate the result and adjust its strategy. In one test, for example, it changed from a three-finger to a four-finger grasp after an unsuccessful attempt and subsequently completed the task.

From high-density data to continuous learning

ACE ROBOTICS also introduced what it calls the “Information-Density Law” for embodied models: the value of data is determined not only by its volume, but by whether it contains information capable of changing the outcome of an agent’s actions.

ACE ROBOTICS classifies embodied data across five information-density levels, from L1 to L5. L1 and L2 data support foundational pre-training; L3 and L4 data remain focused on known tasks; and at L5, data incorporates three-dimensional force and tactile signals, failure-recovery trajectories and variables from open environments. The company says these higher-density forms of interaction data support stronger generalization, reflection and continuous learning.

Built on this principle, Ambient Capture Engine 2.0 is a human-centric system for capturing, processing and reusing high-density physical-interaction data.

ACE Ego Kit is a lightweight wireless wearable comprising head, hand and chest components. It includes the ACE Sense Glove, which offers sensitivity of 0.01 newtons and a joint-angle error of less than two degrees. The system can synchronize data from more than 20 heterogeneous sensors with a timing error of less than one millisecond.

ACE Data Engine is an automated data-production platform for continuous, high-precision annotation of long-horizon tasks. It incorporates ACE-ViDiHand, a generative 4D hand-motion-capture framework designed to track movements despite occlusion and rapid motion, which ACE ROBOTICS reports led across three public benchmarks (ARCTIC, HOT3D and HOI4D) as of July 2026. The company recorded a frame-level accuracy of 0.997 and up to a 4.8-fold improvement in motion smoothness.

ACE Ego Matrix standardizes embodied data across four dimensions: spatial coordinates, embodiment structure, action timing and data quality. It is designed to align data collected by different operators, devices and robot platforms for reuse across systems. ACE ROBOTICS has open-sourced the framework, which it says placed first on the RoboCase and RoboTwin leaderboards.

ACE ROBOTICS also announced the open release of ACE-Data-0, an L5 household-interaction dataset featuring complex physical tasks and high-precision annotations.

Three solutions moving into commercial operation

Building on its data infrastructure and Kairos world-model capabilities, ACE ROBOTICS introduced three standardized industry solutions designed for deployment within existing commercial workflows.

Xiaoman, its integrated fulfillment solution for instant retail, is paired with the new W1 fulfillment robot. The W1 has a robot-to-payload weight ratio of less than 2:1, force-control precision within one newton and a minimum required aisle width of 75 centimeters, making it suitable for high-density shelving environments. It is able to grasp both flexible packaging and rigid goods.

The solution connects robot hardware, world-model capabilities, motion control, product information, warehouse locations and order management. It supports lightweight deployment within days across flash-fulfillment warehouses, small-format stores and integrated warehouse-store setups. It has been deployed with customers including Sense MartGo, Kuaikeda and PetroChina convenience stores, using real order and shelf data to improve fulfillment performance. ACE ROBOTICS plans to deploy the solution across 1,000 retail locations over the next year and expand to approximately 10,000 stores within two years.

Xiaoxin addresses hotel-laundry operations, where ACE ROBOTICS estimates that approximately 80% of demand occurs at night.

The solution enables robots to handle collection, loading, unloading, washing, sorting and folding, with ironing capabilities planned for future versions. It is designed to reduce night-shift staffing pressure and improve operational consistency across non-standard hotel laundry environments.

Xiaotu is designed for autonomous operation in complex outdoor environments. Using a “one brain, many bodies” architecture, it applies a general-purpose intelligent brain (Agentic AI) across multiple quadruped robot platforms.

The system supports indoor and outdoor navigation, dynamic obstacle avoidance, autonomous charging, remote dispatch and multi-robot coordination. It has been deployed in visitor-guidance and interactive experience services at cultural-tourism events, including the Begonia Flower Festival in Tianjin.

Building an open embodied AI ecosystem

ACE ROBOTICS also announced a series of partnerships spanning infrastructure, computing and commercial deployment.

The company entered into a strategic collaboration with the Caohejing Development Zone to develop an embodied AI innovation platform.

In retail and fulfillment, ACE ROBOTICS announced collaborations with PetroChina, Kuaikeda and SenseTime Shanhui covering autonomous warehouse stores, front warehouses and unmanned retail environments.

To support the computing requirements of world-model development, ACE ROBOTICS launched the World Model Cloud Ecosystem initiative with Baidu AI Cloud, Alibaba Cloud, Huawei Cloud, Tencent Cloud and SenseCore AI Cloud.

By connecting high-density data, unified world models, robot platforms, commercial applications and shared evaluation standards, ACE ROBOTICS aims to support the transition of embodied AI from individual technical demonstrations to sustained operations in real-world environments.

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About ACE ROBOTICS – Equipping robots with intelligent “brains” and engaging “souls”

ACE ROBOTICS is a pioneering robotics company dedicated to advancing the field of embodied intelligence. Through breakthrough technological innovations and deep insights into embodied intelligence scenarios, we aim to empower robots with the ability to autonomously understand and explore the physical world, thereby accelerating their commercial implementation.

The company pioneered the ACE R&D paradigm and built a vision-based “environmental data engine, real-world cognition, embodied interaction generalization” technology chain. Using full spatiotemporal and multi-perspective environmental capture as its engine, along with Kairos 3.0 – China’s first open-source and commercially applicable world model – plus the Embodied Foundation Model as its technical backbone, ACE ROBOTICS addresses core industry challenges such as data scarcity, common sense gaps, poor generalization, and limited versatility. Simultaneously, the company unveiled its flagship A1 Embodied Super Brain Module, accelerating the large-scale commercial deployment of embodied intelligence across diverse scenarios.

ACE ROBOTICS is both a technology pioneer and an ecosystem builder. Through strategic cooperation with top hardware manufacturers, cloud service providers, and vertical scenario partners, we have broken through the “model-hardware-scenario” industrial deadlock, providing standardized and customized solutions that are driving the development of China’s embodied intelligence industry.

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The facility, announced on 7 July, is one of DXC’s largest global delivery hubs and brings together customer collaboration areas, an AI Hub, cybersecurity facilities, forensics labs, a Security Operations Centre and a Network Operations Centre. The centre is designed to help clients identify high-value AI use cases, test prototypes, integrate them with legacy platforms and operate them at scale.

The move comes as large companies are under pressure to modernise complex technology estates without disrupting core systems that run banking, insurance, manufacturing, public sector services, travel and healthcare operations. For global IT services firms, the most valuable opportunity now lies not only in building new AI tools, but in attaching them safely to ageing systems of record, cloud platforms, data pipelines and cybersecurity controls.

DXC said the Bengaluru site includes immersive Customer Experience Zones, Fluid Collaboration Hubs, Ideation Studios, Co-Creation Labs and Partner Experience Zones. The structure signals a shift in IT outsourcing from back-office delivery to co-engineering, where clients, consultants and technology partners work together on proof-of-value projects before wider deployment.

Ramnath Venkataraman, president of consulting and engineering services at DXC Technology, said the centre would bring engineering talent closer to customers and partners. He said the objective was to “co-create, engineer, and scale AI-powered solutions” that address complex business challenges and move clients faster from ideas to outcomes.

Rob Le Busque, DXC’s president for Asia Pacific and Japan, described the centre as a base for AI innovation, engineering and customer collaboration. He said DXC was aiming to help customers build connected enterprises where people work alongside AI agents to engineer and run systems of record.

The investment reflects Bengaluru’s continuing pull as a global technology hub. The city hosts a dense base of engineering talent, multinational technology development centres, enterprise software teams and start-up ecosystems. DXC already employs about 38,000 people in the country, making the new facility a strategic addition rather than a symbolic office expansion.

For DXC, the centre also arrives at a critical phase in its own turnaround. The company reported revenue of $12.64bn for fiscal 2026, down 1.8 per cent year on year, while adjusted EBIT stood at $970m. Fourth-quarter revenue was $3.13bn, down 1.2 per cent year on year, though bookings reached $3.3bn and the book-to-bill ratio stood at 1.07 times. Those figures underline both the pressure on legacy IT services revenue and the importance of higher-value modernisation work.

The enterprise AI market has moved beyond experimental chatbots and isolated productivity pilots. Large organisations are now seeking systems that can automate service operations, assist software engineering, improve fraud detection, support call-centre workflows, optimise supply chains and strengthen cyber response. The challenge is that many of these functions depend on decades-old enterprise platforms, heavily customised databases and compliance-heavy workflows.

That is where Bengaluru’s new DXC centre is positioned. Its AI Hub is intended to support hands-on development and testing, while the cyber and operations facilities allow clients to evaluate whether AI tools can be monitored, secured and maintained after deployment. This full-lifecycle approach is becoming central to enterprise modernisation, especially in sectors where downtime, data leakage or flawed automation can carry heavy financial and regulatory consequences.

The facility also strengthens DXC’s partner-led model. Its Partner Experience Zones are expected to support work with software, cloud, cybersecurity and platform companies. For IT services providers, alliances with hyperscalers and enterprise software vendors have become essential as clients seek hybrid solutions rather than single-vendor transformation programmes.

The debate has sharpened as enterprises test AI systems for strategy modelling, risk alerts, market forecasting, compliance reviews and scenario planning. These tools promise faster decisions and broader analysis, but they also raise questions over directors’ duties, audit trails and responsibility when algorithmic advice shapes corporate choices.

Singapore has positioned itself as one of Asia’s most active AI governance centres, combining public investment, industry adoption programmes and voluntary frameworks rather than sweeping legislation. The approach is designed to encourage innovation while keeping human accountability at the centre of AI deployment. That balance is now being tested as companies examine whether AI can move beyond back-office functions and become a structured part of board deliberations.

The issue has gained urgency because AI systems are no longer limited to producing summaries or automating routine workflows. Newer agentic AI tools can plan tasks, reason across data, trigger actions and interact with software systems with limited human intervention. For corporate boards, such capability creates both opportunity and exposure. A system that can flag liquidity pressure, supply-chain disruption or reputational risk may improve oversight. A system that gives flawed recommendations based on incomplete data, hidden bias or weak assumptions may deepen governance failures.

Singapore’s corporate law framework continues to place responsibility on human directors. Directors are expected to act honestly, use reasonable diligence and discharge their duties in the company’s interests. That obligation does not shift to software vendors, consultants or internal AI teams merely because a board relied on machine-generated analysis. If directors accept AI output without challenge, the legal and reputational risk remains with the board.

Regulators have therefore focused on controls rather than replacement. Financial institutions already face sharper expectations because AI is being used in credit, insurance, fraud detection, wealth management and customer engagement. The fairness, ethics, accountability and transparency principles promoted for data-driven finance have helped shape how firms assess AI systems, particularly where customer outcomes may be affected. Proposed AI risk-management guidance for the sector points towards stronger board oversight, model governance, validation, monitoring and escalation procedures.

For non-financial companies, the same discipline is becoming harder to avoid. Listed companies must show that boards oversee strategy, risk and management performance. As AI begins to influence capital allocation, workforce planning, cyber-risk management and mergers, directors may need to demonstrate that they understand what the tools can and cannot do. Board papers generated by AI may require clear labelling, version control and records of the human judgement applied before decisions are approved.

The government’s wider AI strategy has added momentum. Singapore has refreshed national AI priorities, expanded support for enterprises and set out plans to help thousands of businesses adopt AI meaningfully. Public investment in AI research, computing capacity and talent is intended to strengthen national competitiveness, while partnerships with major technology firms are drawing more AI activity into the city-state.

Corporate leaders are watching these moves closely. Singapore’s appeal as a trusted business hub depends partly on whether it can offer companies a stable governance environment for AI adoption. The city-state has avoided a heavily prescriptive regime, preferring practical frameworks, testing tools and assurance mechanisms. That has helped businesses experiment, but it also means boards cannot simply wait for detailed rules before acting.

A growing concern is explainability. Board decisions often involve judgement under uncertainty, but directors must still be able to justify their reasoning. AI tools can generate polished recommendations without revealing how conclusions were reached or which data points carried the most weight. That creates problems for audit committees, external auditors and regulators when a decision later comes under scrutiny.

Another risk is over-reliance. Directors may treat AI output as neutral because it appears data-driven, even when the underlying model reflects skewed training data, weak prompts or commercial assumptions built by vendors. Smaller enterprises face an added challenge because they may lack internal expertise to test systems independently. For them, AI adoption can depend heavily on third-party products whose limitations are not always clear.

Singapore’s emerging answer is likely to rest on disclosure, assurance and accountability. AI “nutrition labels” under discussion would help users understand intended uses and limitations of AI products. Testing frameworks and accredited evaluation bodies could make it easier for companies to compare tools and identify risks before deployment. These measures would not remove directors’ obligations, but they could give boards a clearer basis for responsible adoption.