Robotics
AI Platform for Robotics
For the teams building the next generation of physical intelligence. Mixtrain provides the platform purpose-built to handle multi-modal robotics data to train, evaluate, and deploy models from end to end.
Bring your training scripts. We'll provide the infrastructure.
Robotics teams shouldn't spend months building data pipelines and evaluation harnesses. Mixtrain handles the infrastructure so you can iterate on what matters — your models.
Multi-modal by default
Camera, LiDAR, IMU, force/torque, tactile — version and manage datasets across sensor modalities in a single workspace. Slice by episode, trajectory, or time range. Diff across versions.
Evaluate before you deploy
Define safety constraints, success metrics, and transfer quality benchmarks. Run evaluations across simulation environments automatically. Catch regressions before they reach hardware.
Reproducible by design
Every dataset version, training config, checkpoint, and evaluation result is tracked. Re-run any experiment from any point in your project history. No more “it worked on my machine.”
Close the sim-to-real gap
The gap between simulation performance and real-world behavior is where most robotics projects stall. Domain randomization helps, but you need systematic evaluation to know what transfers and what doesn't.
Mixtrain gives you the tools to measure transfer quality directly — compare model behavior across sim and real data, track domain gap over training, and evaluate randomization strategies side-by-side. When you deploy, you know exactly where the model works and where it doesn't.
What you get
Versioned multi-modal datasets
Store and version datasets with mixed sensor modalities. Parquet, video, point clouds — all tracked together.
Custom evaluation pipelines
Define task success, safety constraints, motion quality, and transfer metrics. Run them on every checkpoint.
Distributed training
Launch multi-GPU and multi-node training jobs with your own scripts or Mixtrain recipes.
Reward function iteration
Compare reward variants side-by-side with evaluation pipelines that measure what you care about.
Safety constraint checking
Define collision boundaries, force limits, and workspace restrictions. Models that violate constraints don't pass evaluation.
Checkpoint management
Save, compare, and deploy any checkpoint. Roll back instantly if a deployment underperforms.
Integration with sim frameworks
Works with Isaac Sim, MuJoCo, PyBullet, and custom simulation environments.
Edge deployment export
Export optimized models for deployment on robot hardware with latency profiling and ONNX/TensorRT support.
Start training
Get your robotics models from simulation to the real world. Free to start.