QUANTA X1

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Product Description

The Quanta X1 is a wheeled bimanual robot independently developed by X Square, featuring a wheeled chassis and lightweight arms with a working range of 0–1 m. Equipped with the WALL-A Embodied Foundation Model and supporting multimodal teleoperation, the Quanta X1 is designed for teleoperation, data collection and model validation in research and education.

Product Specifications

The Quanta X1 robot features a total of 20 degrees of freedom (DOF), with a height of 97 cm and a working range of 0–1 m, whilst its torso has 6 DOF. Its single arm offers a reach of 620 mm and a payload capacity of 1.5 kg; the end-effector includes a gripper with 2 DOF and a dexterous hand with 20 DOF. The mobile chassis delivers a mobility speed of 1 m/s. For perception and computing, it is equipped with a multi-sensor system encompassing 2D laser radar, IMU, ultrasound and other sensing modalities, alongside a dedicated processor configuration to support its operational capabilities.

Company Profile

Independent Variable Robotics Technology (Shenzhen) Co., Ltd.

Zijian Robot focuses on developing its own “general-purpose embodied intelligence large model” to build general-purpose robots capable of precise manipulation; it is one of the first companies in China to adopt a fully end-to-end approach to realising a general-purpose embodied intelligence large model. The company’s self-developed WALL-A, the foundational embodied intelligence model within the “GreatWall” series of manipulation large models, possesses autonomous perception, decision-making and high-precision manipulation capabilities, and has achieved global leadership across multiple dimensions. Concurrently, the company adheres to the principle of synchronous iteration of both software and hardware, independently developing and continuously optimising robot platforms tailored for multi-modal large-model control to better meet the demands of precise manipulation and stable operation in open environments. Currently, Zibianli’s robot platforms have been successfully deployed in multi-step complex task scenarios.
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