Skip to Main content Skip to Navigation
Journal articles

Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics

Rituraj Kaushik 1 Pierre Desreumaux 1 Jean-Baptiste Mouret 1
1 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : 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 base-lines. 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.
Complete list of metadata

Cited literature [38 references]  Display  Hide  Download

https://hal.inria.fr/hal-02462935
Contributor : Jean-Baptiste Mouret <>
Submitted on : Friday, March 13, 2020 - 10:46:12 AM
Last modification on : Wednesday, April 28, 2021 - 3:40:05 AM
Long-term archiving on: : Sunday, June 14, 2020 - 1:13:57 PM

File

1907.07029.pdf
Files produced by the author(s)

Identifiers

Citation

Rituraj Kaushik, Pierre Desreumaux, Jean-Baptiste Mouret. Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics. Frontiers in Robotics and AI, Frontiers Media S.A., 2020, 6, ⟨10.3389/frobt.2019.00151⟩. ⟨hal-02462935v3⟩

Share

Metrics

Record views

156

Files downloads

488