# HOTSPOT: An ad hoc teamwork platform for mixed human-robot teams

## TL;DR

This paper introduces HOTSPOT, a new framework for human-robot collaboration without prior coordination, using decision-making and communication modules.

## Contribution

HOTSPOT is the first framework for ad hoc teamwork in human-robot teams, combining decision-making and natural language communication.

## Key findings

- The decision-theoretic module enables task identification and planning in real-world scenarios.
- The communication module successfully parses natural language interactions between robots and humans.
- HOTSPOT demonstrates effective collaboration in a cooperative object collection task.

## Abstract

Ad hoc teamwork is a research topic in multi-agent systems whereby an agent (the “ad hoc agent”) must successfully collaborate with a set of unknown agents (the “teammates”) without any prior coordination or communication protocol. However, research in ad hoc teamwork is predominantly focused on agent-only teams, but not on agent-human teams, which we believe is an exciting research avenue and has enormous application potential in human-robot teams. This paper will tap into this potential by proposing HOTSPOT, the first framework for ad hoc teamwork in human-robot teams. Our framework comprises two main modules, addressing the two key challenges in the interaction between a robot acting as the ad hoc agent and human teammates. First, a decision-theoretic module that is responsible for all task-related decision-making (task identification, teammate identification, and planning). Second, a communication module that uses natural language processing to parse all communication between the robot and the human. To evaluate our framework, we use a task where a mobile robot and a human cooperatively collect objects in an open space, illustrating the main features of our framework in a real-world task.