Knowledge Acquisition and Completion for Long-Term Human-Robot Interactions using Knowledge Graph Embedding

TL;DR

This paper presents a continual learning architecture for robots in human-robot interaction, using knowledge graph embeddings to incrementally acquire and complete environmental knowledge from sparse user data over long-term interactions.

Contribution

It introduces a novel architecture that combines continual learning with knowledge graph embedding to enhance long-term environmental understanding in robots.

Findings

Effective incremental learning of entities and relations

Improved knowledge representation over multiple sessions

Robustness to sparse and dynamic data environments

Abstract

In Human-Robot Interaction (HRI) systems, a challenging task is sharing the representation of the operational environment, fusing symbolic knowledge and perceptions, between users and robots. With the existing HRI pipelines, users can teach the robots some concepts to increase their knowledge base. Unfortunately, the data coming from the users are usually not enough dense for building a consistent representation. Furthermore, the existing approaches are not able to incrementally build up their knowledge base, which is very important when robots have to deal with dynamic contexts. To this end, we propose an architecture to gather data from users and environments in long-runs of continual learning. We adopt Knowledge Graph Embedding techniques to generalize the acquired information with the goal of incrementally extending the robot's inner representation of the environment. We evaluate the performance of the overall continual learning architecture by measuring the capabilities of the robot of learning entities and relations coming from unknown contexts through a series of incremental learning sessions.