# Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics

arXiv: https://arxiv.org/abs/1907.07029 · HTML: https://mlsanity.com/paper/1907.07029/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires](https://mlsanity.com/paper/2109.08522/) (2021)
- [Accelerated Quality-Diversity through Massive Parallelism](https://mlsanity.com/paper/2202.01258/) (2022)
- [SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation](https://mlsanity.com/paper/2201.13248/) (2022)
- [Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms](https://mlsanity.com/paper/2204.09828/) (2022)
- [Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity](https://mlsanity.com/paper/2210.09918/) (2022)
- [Hierarchical Quality-Diversity for Online Damage Recovery](https://mlsanity.com/paper/2204.05726/) (2022)
- [Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains](https://mlsanity.com/paper/2304.03672/) (2023)
- [A Simple Approach to Continual Learning by Transferring Skill Parameters](https://mlsanity.com/paper/2110.10255/) (2021)
- [Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors](https://mlsanity.com/paper/2210.04819/) (2022)
- [Discovering Unsupervised Behaviours from Full-State Trajectories](https://mlsanity.com/paper/2211.15451/) (2022)
- [From Tabula Rasa to Emergent Abilities: Discovering Robot Skills via Real-World Unsupervised Quality-Diversity](https://mlsanity.com/paper/2508.19172/) (2025)

## Abstract (arXiv)

Repertoire-based learning is a data-efficient adaptation approach based on a two-step process in which (1) a large and diverse set of policies is learned in simulation, and (2) a planning or learning algorithm chooses the most appropriate policies according to the current situation (e.g., a damaged robot, a new object, etc.). In this paper, we relax the assumption of previous works that a single repertoire is enough for adaptation. Instead, we generate repertoires for many different situations (e.g., with a missing leg, on different floors, etc.) and let our algorithm selects the most useful prior. Our main contribution is an algorithm, APROL (Adaptive Prior selection for Repertoire-based Online Learning) to plan the next action by incorporating these priors when the robot has no information about the current situation. We evaluate APROL on two simulated tasks: (1) pushing unknown objects of various shapes and sizes with a robotic arm and (2) a goal reaching task with a damaged hexapod robot. We compare with "Reset-free Trial and Error" (RTE) and various single repertoire-based baselines. The results show that APROL solves both the tasks in less interaction time than the baselines. Additionally, we demonstrate APROL on a real, damaged hexapod that quickly learns to pick compensatory policies to reach a goal by avoiding obstacles in the path.