A Unified Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams

• URL: http://arxiv.org/abs/2301.08038v2
• Date: Mon, 25 Sep 2023 11:52:26 GMT
• Title: A Unified Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams
• Authors: Edoardo Lamon (1,2), Fabio Fusaro (1,3), Elena De Momi (1,3), Arash Ajoudani (1) ((1) Human-Robot Interfaces and Interaction, Istituto Italiano di Tecnologia, Genoa, Italy, (2) Department of Information Engineering and Computer Science, Universit\`a di Trento, Trento, Italy, (3) Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy)
• Abstract summary: We present a novel architecture for dynamic role allocation and collaborative task planning in a mixed human-robot team of arbitrary size. The architecture capitalizes on a centralized reactive and modular task-agnostic planning method based on Behavior Trees (BTs) Different metrics used as MILP cost allow the architecture to favor various aspects of the collaboration.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing deployment of human-robot collaborative processes in several industrial applications, such as handling, welding, and assembly, unfolds the pursuit of systems which are able to manage large heterogeneous teams and, at the same time, monitor the execution of complex tasks. In this paper, we present a novel architecture for dynamic role allocation and collaborative task planning in a mixed human-robot team of arbitrary size. The architecture capitalizes on a centralized reactive and modular task-agnostic planning method based on Behavior Trees (BTs), in charge of actions scheduling, while the allocation problem is formulated through a Mixed-Integer Linear Program (MILP), that assigns dynamically individual roles or collaborations to the agents of the team. Different metrics used as MILP cost allow the architecture to favor various aspects of the collaboration (e.g. makespan, ergonomics, human preferences). Human preference are identified through a negotiation phase, in which, an human agent can accept/refuse to execute the assigned task.In addition, bilateral communication between humans and the system is achieved through an Augmented Reality (AR) custom user interface that provides intuitive functionalities to assist and coordinate workers in different action phases. The computational complexity of the proposed methodology outperforms literature approaches in industrial sized jobs and teams (problems up to 50 actions and 20 agents in the team with collaborations are solved within 1 s). The different allocated roles, as the cost functions change, highlights the flexibility of the architecture to several production requirements. Finally, the subjective evaluation demonstrating the high usability level and the suitability for the targeted scenario.

Related papers

• Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration [51.452664740963066]
  Collaborative Gym is a framework enabling asynchronous, tripartite interaction among agents, humans, and task environments. We instantiate Co-Gym with three representative tasks in both simulated and real-world conditions. Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in task performance.
  arXiv  Detail & Related papers  (2024-12-20T09:21:15Z)
• EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents [33.77674812074215]
  We introduce a novel multi-agent framework designed to enable effective collaboration among heterogeneous robots. We propose a self-prompted approach, where agents comprehend robot URDF files and call robot kinematics tools to generate descriptions of their physics capabilities. The Habitat-MAS benchmark is designed to assess how a multi-agent framework handles tasks that require embodiment-aware reasoning.
  arXiv  Detail & Related papers  (2024-10-30T03:20:01Z)
• Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots [1.1049608786515839]
  We propose a Cooperative and Asynchronous Transformer-based Mission Planning (CATMiP) framework to coordinate distributed decision making among agents. We evaluate CATMiP in a 2D grid-world simulation environment and compare its performance against planning-based exploration methods.
  arXiv  Detail & Related papers  (2024-10-08T21:14:09Z)
• COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models [49.24666980374751]
  COHERENT is a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems. A Proposal-Execution-Feedback-Adjustment mechanism is designed to decompose and assign actions for individual robots. The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency.
  arXiv  Detail & Related papers  (2024-09-23T15:53:41Z)
• BMW Agents -- A Framework For Task Automation Through Multi-Agent Collaboration [0.0]
  We focus on designing a flexible agent engineering framework capable of handling complex use case applications across various domains. The proposed framework provides reliability in industrial applications and presents techniques to ensure a scalable, flexible, and collaborative workflow for multiple autonomous agents.
  arXiv  Detail & Related papers  (2024-06-28T16:39:20Z)
• Large Language Model-based Human-Agent Collaboration for Complex Task Solving [94.3914058341565]
  We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving. We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
  arXiv  Detail & Related papers  (2024-02-20T11:03:36Z)
• Unified Human-Scene Interaction via Prompted Chain-of-Contacts [61.87652569413429]
  Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality. This paper presents a unified HSI framework, UniHSI, which supports unified control of diverse interactions through language commands.
  arXiv  Detail & Related papers  (2023-09-14T17:59:49Z)
• AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors [93.38830440346783]
  We propose a multi-agent framework framework that can collaboratively adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that framework framework can effectively deploy multi-agent groups that outperform a single agent. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups.
  arXiv  Detail & Related papers  (2023-08-21T16:47:11Z)
• Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments [54.39292848359306]
  We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic.
  arXiv  Detail & Related papers  (2023-03-27T01:50:45Z)
• An Integrated Dynamic Method for Allocating Roles and Planning Tasks for Mixed Human-Robot Teams [0.0]
  This paper proposes a novel integrated dynamic method for planning and allocating tasks in mixed human robot teams. The Behavior Tree formulation allows encoding a single job as a compound of different tasks with temporal and logic constraints.
  arXiv  Detail & Related papers  (2021-05-25T16:10:30Z)
• A Hierarchical Architecture for Human-Robot Cooperation Processes [3.9514394345533828]
  We propose a hierarchical human-robot cooperation architecture called FlexHRC+. It provides collaborative robots with an extended degree of autonomy when supporting human operators in high-variability shop-floor tasks.
  arXiv  Detail & Related papers  (2020-09-06T19:55:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.