Skill acquisition

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed robotics

30% complete — judgment-based estimate as of November 7, 2026. Confidence: low. The scale starts at 0% before ChatGPT's public release in 2022 and ends at 100% when AI manages the field's continuing advancement without human participation. This rates the outcome's contribution to that transition, not the percentage of tasks solved, a measured fraction of the field, or time remaining.

The shared scoring rubric uses five equally weighted dimensions: execution, breadth, agenda-setting, validation, and sustained autonomous improvement. Points for this outcome are 10 + 5 + 5 + 5 + 5 = 30/100, in that order; each dimension has a maximum of 20. These component judgments make the estimate inspectable, but are not independently measured scores.

Reason for the estimate: Learning from language coaching supports new-task acquisition with less teleoperation. Human teaching effort and time to a fixed reliability level remain unknown. The dated baseline below supplies the findings and source links used for this assessment. Unmeasured components receive no demonstrated-progress credit; missing evidence does not prove a missing capability.

What would move the score: Track every demonstration and coaching minute; evaluate held-out trials until a prespecified success rate is reached, counting unsuccessful training runs. Higher autonomy scores additionally require AI to select worthwhile work, validate results and improve the process over repeated cycles without human research management, repair or adjudication. Lower the score if replication fails, quality regresses or previously hidden human assistance is found. Preserve this dated estimate when adding future assessments.

Measurement and acceptance rule

Demonstrations, robot-hours, and human hours required to learn a new task to a prespecified reliability target.

Evidence requirements

Use randomized task selection and complete trial logs or video, not selected demonstrations. Separate simulation from physical tests, and supervised operation from autonomy. Define what counts as damage or a near miss. Zero incidents in a small trial is not proof of a low underlying incident rate; report exposure and uncertainty.

Related milestones

See the area's proposed milestones. Proposed targets are distinct from the dated evidence below.

Baseline as of November 7 2026

Status: Learning from language coaching demonstrated. Sources accessed November 7, 2026. This is a documentary baseline, not an independent replication or an assertion of the worldwide best result. An unknown measurement is not zero.

Dated findings

April 16, 2026: The developer reports training a high-level policy from repeated language coaching to execute an appliance task without additional teleoperation. Physical Intelligence: pi0.7.

Interpretation and tracking value

No complete demonstrations/robot-hours/human-hours-to-fixed-reliability baseline is established by that example. No new teleoperation does not mean no human teaching.

Next comparable measurement

Track every demonstration and coaching minute; evaluate held-out trials until a prespecified success rate is reached, counting unsuccessful training runs. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.