From task definition
to useful evidence.
We explore how task generation, structured demonstrations and evaluation can work together as a more legible development loop. The guiding questions: which variation matters, what context makes a trajectory reusable, and how can failure guide the next experiment?
Read concept notesSimulation methodology
Describe task spaces and vary conditions with intent.
Dataset concepts
Preserve task, trajectory and provenance context.
Evaluation concepts
Study behavior beyond a familiar training setup.
Designing Diverse Tasks for Generalizable Robot Learning
A concept note on varying scenes, objects and goals to probe what a robot policy has actually learned.
From Simulation Trajectories to Structured Training Data
A concept note about preserving context as demonstrations move from an interaction to a training-ready record.
Evaluating Robot Policies Beyond the Training Environment
A concept note on testing policies against shifted objects, scenes and task conditions instead of one familiar setup.
Explore the concepts behind the stack.
Start with the architecture, methodology and interface concepts currently described on this site.
Documentation overview