# Vector Autoregressive POMDP Model Learning and Planning for Human-Robot Collaboration

## TL;DR

This paper introduces the VAR-POMDP model, an extension of POMDP that captures observation correlations, enabling more expressive and tractable modeling for human-robot collaboration, with effective learning and planning methods validated through experiments.

## Contribution

It proposes the VAR-POMDP model that extends traditional POMDPs by incorporating observation correlations, and demonstrates effective learning and planning techniques for HRC.

## Key findings

- The VAR-POMDP model is more expressive than traditional POMDPs.
- Bayesian non-parametric learning effectively identifies human states.
- Experimental validation shows improved safety and reachability planning.

## Abstract

Human-robot collaboration (HRC) has emerged as a hot research area at the intersection of control, robotics, and psychology in recent years. It is of critical importance to obtain an expressive but meanwhile tractable model for human beings in HRC. In this paper, we propose a model called Vector Autoregressive POMDP (VAR-POMDP) model which is an extension of the traditional POMDP model by considering the correlation among observations. The VAR-POMDP model is more powerful in the expressiveness of features than the traditional continuous observation POMDP since the traditional one is a special case of the VAR-POMDP model. Meanwhile, the proposed VAR-POMDP model is also tractable, as we show that it can be effectively learned from data and we can extend point-based value iteration (PBVI) to VAR-POMDP planning. Particularly, in this paper, we propose to use the Bayesian non-parametric learning to decide potential human states and learn a VAR-POMDP model using data collected from human demonstrations. Then, we consider planning with respect to PCTL which is widely used as safety and reachability requirement in robotics. Finally, the advantage of using the proposed model for HRC is validated by experimental results using data collected from a driver-assistance test-bed.