Technology

DEEPMIRROR has developed an end-to-end visual localization platform for spatial AI that builds and continuously updates its own maps across indoor and outdoor environments. Combined with a real-time autonomous motion-planning system that integrates perception and planning, it forms China’s first vision-only, end-to-end robot navigation solution built on an architecture comparable to Tesla FSD.

Deep learning-based 6-DoF visual pose estimation delivers centimeter-level global localization across large-scale environments.

Self-developed 6-DoF vision foundation model enables automated 3D mapping and incremental map updates.

Semantic global path planning works with model predictive control-based local obstacle avoidance to optimize trajectories in milliseconds and navigate safely around dynamic obstacles.

To support fully autonomous embodied robots across both mobility and task execution, DEEPMIRROR has built a complete autonomous manipulation stack that fuses vision with force control. The system closes the loop from target perception and motion planning to execution control.

A closed-loop operational-space control model enables high-precision hybrid position–force control.

Fine-grained, instance-level semantic segmentation and target pose estimation enable millimeter-level pose tracking for manipulation tasks.

Self-developed spatial AI data acquisition platform maps fine-grained human motion trajectories and force feedback directly to robot arm control.

DEEPMIRROR has developed an end-to-end visual localization platform for spatial AI that builds and continuously updates its own maps across indoor and outdoor environments. Combined with a real-time autonomous motion-planning system that integrates perception and planning, it forms China’s first vision-only, end-to-end robot navigation solution built on an architecture comparable to Tesla FSD.

Industrial-grade durability meets the demands of high-vibration legged robots and other challenging operating conditions.

Hardware–software co-optimization reduces end-to-end latency across perception, decision-making, and control to under 10 ms.

Algorithms are purpose-optimized for heterogeneous on-device computing, allowing core workloads such as VSLAM, depth estimation, and semantic segmentation to run entirely at the edge.

DEEPMIRROR has built an end-to-end AI training and iteration framework for embodied robotics. Powered by China’s first spatial AI data acquisition platform built entirely around dedicated AI compute, the framework connects data acquisition, model training, and algorithm iteration in one continuous pipeline, accelerating the development and ongoing improvement of robot navigation and manipulation capabilities.

Policy optimization for continuous action spaces continuously improves navigation accuracy, manipulation compliance, environmental adaptability, and exception handling.

End-to-end, self-improving data loop inspired by shadow mode automates operational data acquisition, edge-case mining, incremental model training, and OTA algorithm updates.

Self-developed dataset containing tens of millions of real-world household and industrial action samples supports self-supervised visual pretraining and significantly improves model generalization.