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RoboForce Tech Stack
Physical AI and robotic systems for industrial deployment
RoboForce builds AI-driven robotic systems for industrial environments, running a ML-heavy stack (Python, PyTorch, JAX, TensorRT, CUDA) paired with robotics simulation and control tools (SolidWorks, AutoCAD). The project list—vision-language-action models, world models, sim-to-real transfer, deterministic safety layers—reveals a company focused on closing the gap between learned AI behaviors and safe real-world robot operation. Pain points around scalable heterogeneous data storage, multimodal pipelines, and sim-to-real transfer signal active work on the fundamental data and infrastructure challenges of deploying physical AI at scale.
Tech Stack 65 technologies
Core StackPython PyTorch AWS Weights & Biases MLflow TensorFlow C++ Docker Kubernetes PostgreSQL MySQL BigQuery Elasticsearch Redis SolidWorks AutoCAD Jira JAX TensorRT CUDA GCS HDF5 Zarr bash YOLO Google Scholar GCP Azure NVMe Arena PLM+35 more
What RoboForce Is Building
◆Challenges
- Scalable storage for heterogeneous robot data
- End-to-end multimodal data pipelines
- Closing loop for model retraining
- Scalable deployment of industrial robots
- Safe mobility on unpredictable terrain
- Integrating ai outputs with real-time control
- Deterministic safety layer for ai hallucinations
- Ensuring reliability in unstructured outdoor environments
- Manual testing inefficiencies
- Lack of automated testing infrastructure
▲Active Projects
- Data and learning engine for physical ai stack
- Annotation tooling and data curation workflows
- Post-sft reinforcement learning infrastructure
- Vision-language-action models
- World models
- Simulation and sim-to-real transfer
- Local mobility and safety stack
- C++ pipelines for ai costmap consumption
- Deterministic safety layer for ai hallucinations
- Real-world robotic system development
Hiring Activity
8 roles · 8 in 30d
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