In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.

/pdf/coordinating-human-robot-collaboration-by-eeg-based-human-2o4rlarf.pdf

Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control

Human intention prediction is vital for the efficiency of human-robot collaboration (HRC) and is usually modeled based on data-driven methods. However, due to the complexity and diverse nature of HRC, data collection for human intention prediction suffers from low sampling efficiency which restricts the application of HRC in manufacturing. Different from traditional real world data collection, a digital modeling method for HRC is proposed in this paper to generate virtual HRC data. The dynamic musculoskeletal model of human is adopted to simulate the musculoskeletal dynamics of human. The metabolic energy consumption of human is computed and used as an indicator to evaluate the reality of the generated virtual data. The virtual data are used to train human intention prediction model and compared with experimental data. Experimental results show the reality of virtual data and its effectiveness for human intention modeling in human-robot collaborative assembly. The proposed method has potential for reducing the cost of data collection compared with purely experiments. 

Virtual data generation for human intention prediction based on digital modeling of human-robot collaboration

Background: In the context of Warehouse Management Systems, knowledge related to motion trajectory prediction methods utilizing machine learning techniques seems to be scattered and fragmented. Objective: This study seeks to fill this research gap by using a systematic literature review approach. Methods: Based on the data collected from Google Scholar, a systematic literature review was performed, covering the period from 2016 to 2023. The review was driven by a protocol that comprehends inclusion and exclusion criteria to identify relevant papers. Results: Considering the Warehouse Management Systems, five categories of motion trajectory prediction methods have been identified: Deep Learning methods, probabilistic methods, methods for solving the Travelling-Salesman problem (TSP), algorithmic methods, and others. Specifically, the performed analysis also provides the research community with an overview of the state-of-the-art methods, which can further stimulate researchers and practitioners to enhance existing and develop new ones in this field. 

Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review

Reaching a consensus on the team plans is vital to human-AI coordination. We suggest incorporating external models to assist humans in understanding the intentions of AI agents when the AI has no explainable plan to communicate. In this paper, we propose a two-stage paradigm that first trains a Theory of Mind (ToM) model from collected offline trajectories of the target agent and utilizes the model in the process of human-AI collaboration by real-timely displaying the future action predictions of the target agent. We further implement a transformer-based predictor as the ToM model and develop an extended online human-AI collaboration platform for experiments. Experimental results validate that our ToM model can significantly improve team performance, demonstrating the potential of our paradigm in human-AI collaboration.

https://export.arxiv.org/pdf/2405.02229v1.pdf

On the Utility of External Agent Intention Predictor for Human-AI Coordination

Augmenting Human-Robot Collaboration Task by Human Hand Position Forecasting

Human-Robot collaboration (HRC) is an important topic for manufacturing and household robotics. It is very challenging to ensure both efficiency and safety in HRC. This paper presents an HRC pipeline that generates efficient and collision-free robot trajectories based on predictions of the human arm and hand (AH) motions. We train a recurrent neural network for AH trajectory prediction based on observed initial trajectory segments. To increase the accuracy of target estimation at an early stage, the observed and the predicted hand palm trajectory are combined to predict the current AH motion target using Gaussian Mixture Models (GMMs). An optimization-based trajectory generation algorithm is proposed to ensure the safety of the human while collaborating with the robot. The proposed system is validated in a shared-workspace scenario with human pick-and-place motions. The task can be safely and efficiently completed. The results demonstrate that our proposed pipeline can predict the human AH trajectory and estimate the motion target intended by the human accurately and early. 

/pdf/efficient-and-collision-free-human-robot-collaboration-based-g13d88tf.pdf

Efficient and Collision-Free Human-Robot Collaboration Based on Intention and Trajectory Prediction

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Michael Gorner

Author Tools

Chat about Author

Papers

Efficient and Collision-Free Human-Robot Collaboration Based on Intention and Trajectory Prediction