A pair of co-designed, three-finger, nine-DoF dexterous manipulation interface: one wearable for data collection, one for robot deployment.
A generative world model for action-controllable, long-horizon robotic simulation. Built on next-scale autoregression with scale-compressed streaming memory and on-policy alignment, it generates visual futures in a coarse-to-fine manner, achieving calibrated task outcomes and consistent policy rankings across 600+ matched sim-to-real closed-loop rollouts.
Human-to-robot One-Shot Skill Acquisition (HOST)
A training-free framework that enables robots to acquire new manipulation skills in seconds from a single human video while retaining previously mastered capabilities.
A multimodal action tokenizer that doubles as a semantic interface between vision-language reasoning and continuous robot control.
XRZero-G0 is an embodied data acquisition and strategy learning system designed through deep collaboration between hardware and software. It aims to overcome the fundamental bottleneck of acquiring high-quality, motion-aligned demonstration data in the field of dexterous robot operation.
Carving World Action Modeling at Their Event Joints
fully open-source, trained with gradient-bridged co-training, and deployable on real robots straight from pretraining.
An end-to-end embodied foundation model that leverages large-scale multimodal pretraining to achieve (1) embodiment-aware vision--language understanding, (2) strong language--action association, and (3) robust manipulation capability.
自变量机器人致力于研发由端到端大模型驱动的通用机器人，聚焦具身大模型的通用性与泛化能力，是国内具身智能基础模型的标杆企业，2026年发布全球首个世界统一模型，并自主研发、持续优化原生适配基础模型控制的机器人本体。公司自研“数据采集-大模型算法-核心零部件-硬件本体”，实现全栈闭环，目前已率先在家庭、物流等真实场景实现通用具身智能技术的落地，长远目标是构建能够精细操作的通用智能机器人。关于我们
全球首个基于世界统一模型架构的具身智能基础模型WALL-B世界统一模型架构（World Unified Model)从“VLA“到”原生多模态融合架构“，单模型适配全模态，全任务
围绕自研的 WALL-A 操作大模型，实现从感知、理解到动作控制的全流程智能统一，彻底突破传统机器人依赖规则与编程的局限。实现复杂环境中的高精度、自主化作业。
资料来源：企业官网公司简介（复核日期：2026-10-07）。