# Synergistic Scheduling of Learning and Allocation of Tasks in Human-Robot Teams

arXiv: https://arxiv.org/abs/2203.07478 · HTML: https://mlsanity.com/paper/2203.07478/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2022.

## Built on by (most cited first, 5 of 5)

- [Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision](https://mlsanity.com/paper/2206.14349/) (2022)
- [Optimal Interactive Learning on the Job via Facility Location Planning](https://mlsanity.com/paper/2505.00490/) (2025)
- [Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation](https://mlsanity.com/paper/2508.05535/) (2025)
- [Formalizing Task-Space Complexity for Zero-Shot Generalization](https://mlsanity.com/paper/2606.20967/) (2026)
- [Deliberate Practice: Learning Robot Skills under a Budget](https://mlsanity.com/paper/2608.13415/) (2026)

## Abstract (arXiv)

We consider the problem of completing a set of n tasks with a human-robot team using minimum effort. In many domains, teaching a robot to be fully autonomous can be counterproductive if there are finitely many tasks to be done. Rather, the optimal strategy is to weigh the cost of teaching a robot and its benefit -- how many new tasks it allows the robot to solve autonomously. We formulate this as a planning problem where the goal is to decide what tasks the robot should do autonomously (act), what tasks should be delegated to a human (delegate) and what tasks the robot should be taught (learn) so as to complete all the given tasks with minimum effort. This planning problem results in a search tree that grows exponentially with n -- making standard graph search algorithms intractable. We address this by converting the problem into a mixed integer program that can be solved efficiently using off-the-shelf solvers with bounds on solution quality. To predict the benefit of learning, we propose a precondition prediction classifier. Given two tasks, this classifier predicts whether a skill trained on one will transfer to the other. Finally, we evaluate our approach on peg insertion and Lego stacking tasks, both in simulation and real-world, showing substantial savings in human effort.