A Modular Framework for Flexible Planning in Human-Robot Collaboration

TL;DR

This paper introduces a modular, scalable framework combining HTN planning and multisensory perception to improve flexible human-robot collaboration in complex, real-world assembly tasks.

Contribution

It proposes a formalism for articulated task modeling and integrates perception and planning modules for enhanced HRC, demonstrating scalability in furniture assembly scenarios.

Findings

Successful implementation of a multisensory perception pipeline

Effective collaboration between humans and robot in assembly tasks

Scalability demonstrated in complex cooperative scenarios

Abstract

This paper presents a comprehensive framework to enhance Human-Robot Collaboration (HRC) in real-world scenarios. It introduces a formalism to model articulated tasks, requiring cooperation between two agents, through a smaller set of primitives. Our implementation leverages Hierarchical Task Networks (HTN) planning and a modular multisensory perception pipeline, which includes vision, human activity recognition, and tactile sensing. To showcase the system's scalability, we present an experimental scenario where two humans alternate in collaborating with a Baxter robot to assemble four pieces of furniture with variable components. This integration highlights promising advancements in HRC, suggesting a scalable approach for complex, cooperative tasks across diverse applications.