Deliberate Practice: Learning Robot Skills under a Budget
We consider the problem of autonomously learning robot skills under a limited practice budget for sequential tasks. We propose an active skill learning algorithm, \emph{Deliberate Practice (DP)}, that computes a provably \emph{budget-optimal} allocation---practicing skills that maximize expected cumulative reward while being learnable within the budget. DP estimates both the time needed to master skills and the cumulative reward of the task plans that the skills unlock. Computing a budget-optimal allocation is challenging as it requires reasoning about combinatorially many skill plans over a large practice budget. Our key contribution is a bilinear program that can compute this exactly us...
| Authors | Shivam Vats, Sudarshan Harithas, Mete Tuluhan Akbulut et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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