Sequor Robotics develops Robotics Foundation Models to enable General Purpose Robot Intelligence across various platforms. Their core AI capabilities empower diverse robotic systems, from mobile units to humanoid forms. This versatile solution allows robots to operate effectively in complex, real-world environments.
Funding
Funding not disclosed
FFounders
Product
Problem
Robotic systems typically rely on bespoke, task‑specific software stacks that require extensive data collection, engineering effort, and platform‑specific tuning, which limits scalability and slows deployment in dynamic, real‑world environments.
Solution
Sequor Robotics delivers a universal Robotics Foundation Model (RFM) that acts as a pre‑trained, multimodal AI core for general‑purpose robot intelligence. The RFM is trained on massive simulation and real‑world datasets and supports transfer learning, enabling rapid fine‑tuning for new tasks with minimal additional data. A standardized edge‑compatible API and runtime allow the model to be integrated into existing robot control architectures—mobile, quadrupedal, or humanoid—without rewriting low‑level code. Built‑in tools facilitate simulation‑to‑real transfer, safety verification, and continuous online learning from operational feedback, accelerating adaptation to unstructured environments. By consolidating perception, planning, and control into a single adaptable model, Sequor reduces integration time, development cost, and the need for platform‑specific AI engineering.
Target Audience
Primary customers are robot manufacturers, system integrators, and research labs building mobile, quadrupedal, or humanoid platforms that require scalable, adaptable AI capabilities.
Features
- Large‑scale pre‑trained foundation model covering vision, proprioception, and language modalities for robotics applications
- Transfer‑learning pipeline that fine‑tunes the core model to new tasks using as few as a few hundred labeled examples
- Edge‑optimized runtime (CUDA, TensorRT, and ARM‑Neural‑Engine support) with a REST/ROS‑compatible API for seamless integration
- Sim‑to‑real adaptation suite including domain randomization, dynamics calibration, and safety‑critical verification checks
- Continuous learning framework that ingests operational telemetry to update policies while preserving safety constraints
- Modular plugin architecture allowing developers to swap perception, planning, or control sub‑modules without retraining the entire model