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Choosing First Tasks

The first tasks you give a humanoid shape the whole programme: what data you collect, how you measure progress and whether people come to trust the robot. This guide offers a method for picking those tasks and estimating the effort each one takes.

By Arjun Rao · Updated October 2026

Scoring candidate tasks before you commit

Write down every job the robot might take on, then score each one against a short set of questions. Does it happen often enough to matter? Is the area around it reasonably stable from day to day? Can a failure be spotted and undone without harm to people or goods? Are the objects within what the robot's hands and arms can hold? A task that scores well on all four makes a better starting point than an impressive one involving fragile stock or a busy walkway. Leave anything that needs close contact with people, sharp tools or heavy loads held near the body until later, once both the robot and your safety process have a track record.

Setting pass marks and trial counts

Agree a written pass mark for each first task before a single demonstration is recorded, so the data effort has a clear target. State the success rate and time per attempt that would make automation worthwhile for you, then fix how many trials you will run and which variations they will include. Toyota Research Institute's study of multitask manipulation policies used blind, randomised trials and an evaluation pipeline designed to compare models with statistical confidence, a reminder that a handful of attempts can flatter or condemn a policy by chance. NIST's grasping and manipulation project, which develops task boards and test methods, shows how a physical test set can make trials repeatable from one software version to the next.

Estimating the cost of the data

Demonstrations cost operator hours, robot time and someone to check the results, so estimate them per task rather than for the programme as a whole. Variety drives the bill more than raw count. Lin and colleagues at Tsinghua University found that a policy's performance in new settings rose roughly as a power law with the number of environments and objects in its training data, and that extra demonstrations per environment added little once a threshold was reached. Plan collection across several locations and object sets instead of endless repeats in one corner. Multiply planned episodes by the minutes each takes, add set-up and review time, then add a margin for the retraining rounds that failures will trigger.

Ordering tasks so each one helps the next

Sequence your first tasks so that later ones reuse what earlier ones built. Two tasks in the same room with the same containers share scene data, calibration work and safety arrangements. Tasks that share a basic motion, such as picking from a shelf, let you test whether a policy trained on one helps with the next. The Toyota Research Institute study also found that multitask pretraining let new tasks be taught with a fraction of the data that single-task baselines needed, which favours building a broad base over polishing one narrow skill for months. Revisit the list each time a task goes live, because real results often change which job deserves to come next.

Sources and further reading

Data Scaling Laws in Imitation Learning for Robotic Manipulation — Tsinghua University and partners (arXiv).
Large real-robot study finding that variety of scenes and objects matters more than piling up demonstrations in one place.
A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation — Toyota Research Institute (arXiv).
Blind, randomised trials of multitask robot policies, with an evaluation pipeline built to judge results with statistical confidence.
Grasping, Manipulation, and Contact Safety Performance of Robotic Systems — National Institute of Standards and Technology (NIST).
Task boards, test methods and metrics NIST is developing to measure robot grasping, dexterity and contact safety.

Common questions

What are good first tasks for a humanoid robot?

Good candidates include moving totes between shelves and a conveyor, sorting items into bins or fetching parts to a workstation. They repeat often, failures are cheap to fix, and they build the data, tooling and confidence that harder jobs can draw on later.

Should the first humanoid task be the one with the biggest payoff?

Rarely. The most valuable jobs tend to be complex, varied or close to people, which makes early failures costly and slow to diagnose. A modest task that succeeds gives you working tools and a trained team, and you can move to higher-value work once results hold.

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