# Composing Option Sequences by Adaptation: Initial Results

arXiv: https://arxiv.org/abs/2409.08195 · HTML: https://mlsanity.com/paper/2409.08195/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models](https://mlsanity.com/paper/2607.16506/) (2026)

## Abstract (arXiv)

Robot manipulation in real-world settings often requires adapting the robot's behavior to the current situation, such as by changing the sequences in which policies execute to achieve the desired task. Problematically, however, we show that composing a novel sequence of five deep RL options to perform a pick-and-place task is unlikely to successfully complete, even if their initiation and termination conditions align. We propose a framework to determine whether sequences will succeed a priori, and examine three approaches that adapt options to sequence successfully if they will not. Crucially, our adaptation methods consider the actual subset of points that the option is trained from or where it ends: (1) trains the second option to start where the first ends; (2) trains the first option to reach the centroid of where the second starts; and (3) trains the first option to reach the median of where the second starts. Our results show that our framework and adaptation methods have promise in adapting options to work in novel sequences.