# A Modular Framework for Flexible Planning in Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2406.04907 · HTML: https://mlsanity.com/paper/2406.04907/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2024.

## Built on by (most cited first, 2 of 2)

- [A Comparative Study of Human Activity Recognition: Motion, Tactile, and multi-modal Approaches](https://mlsanity.com/paper/2505.08657/) (2025)
- [A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration](https://mlsanity.com/paper/2602.07024/) (2026)

## Abstract (arXiv)

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.