AGIBOT Sets AI WAVE for Oct 22, Seven Embodied AI Launches and 24-Hour Store Trial
AGIBOT will launch 'AI WAVE' starting Oct. 22 as a prelude and extension of its 2026 AGIBOT Innovation Day. Over seven release days, the company plans to present seven outputs spanning datasets, an operating platform, a robot body, an Agent framework, a simulation platform, a foundation model and a collective experience learning paradigm. AGIBOT says the sequence is meant to concentrate its full-stack embodied AI technology layout rather than a single product launch.
The releases are positioned as a prelude to real-world testing. On Nov. 2-3, six G2 robots will enter a convenience store for a 24-hour uninterrupted livestream, covering four complete shifts: lunch peak, evening peak, night duty and breakfast preparation. AGIBOT describes the exercise as a 'probation review' for its robots. The company says all released technologies will ultimately be tested in real scenarios.
The event follows industry debate over whether embodied intelligence still has value as general large models grow stronger. The debate has swept the industry in recent months. AGIBOT's answer is not framed as a debate. The company says large-scale robot deployment depends on full-stack coordination, not single-point breakthroughs. A single task requires model-body coordination; long-term stable operation requires environmental adaptation and exception handling; scaled application requires low training cost, fast deployment and capability reuse. In the physical world, data, tools, body, systems and models must all work together.
AI WAVE is designed to put each link on the table and then place robots in a real convenience store for a 24-hour 'job.' AGIBOT says it aims to use full-stack embodied AI as a link to connect the industrial ecosystem, accelerate physical AI deployment and evolution, and help robots enter real-world scenarios and create real value. The company says the effort is not about presentations but about proving that each component works together in physical environments.
The seven-day schedule runs one topic per day. On Oct. 22, AGIBOT WORLD 2026 will release the first batch of its fourth 'Physical Reasoning' dataset, with 150 hours of real-robot data focused on decision-making, aiming to move robots from 'learning to operate' to 'autonomously completing tasks.' On Oct. 23, Genie Studio 2.0 will launch as a one-stop, full-process embodied intelligence operating platform, targeting the shift from 'single-point usability' to 'continuous evolution, standard delivery and scale replication.'
On Oct. 26, AGIBOT will introduce G2 Max, described as a safety-standard heavy-load embodied industrial robot that combines heavy-load capability with sub-millimeter precision. On Oct. 27, Genie Master will connect Agents with the physical world and, according to AGIBOT, achieve Physical RSI (embodied recursive self-improvement) in real business scenarios for the first time.
On Oct. 28, Genie Sim will receive a major upgrade, with a self-developed physics engine intended to overcome flexible-simulation bottlenecks; AGIBOT says real-simulation consistency for complex tasks exceeds 95%. On Oct. 29, the company will unveil a foundation model that it says redefines the evolution boundary of embodied intelligence, with details to be disclosed then. On Oct. 30, Collective Experience Learning (CEL) will be presented as the first robot learning paradigm prototype-validated on a cluster of more than 100 real robots, moving robots from reliance on human demonstration toward collective autonomous growth.
The final stop is the convenience store. From 10 a.m. Nov. 2 to 10 a.m. Nov. 3, six G2 robots will stock shelves, replenish goods, organize products, prepare hot food, collect trash, make coffee and greet customers across four continuous shifts, all livestreamed. AGIBOT says the livestream will show whether the robots can handle peak hours, quiet night shifts and breakfast preparation. The closed loop it is building connects learning, action and continuous evolution: data is accumulated from real interaction, experience is deposited during continuous operation, and new experience feeds the next learning cycle. Every step toward the real world, the company says, is supported by its full-stack technology system.