Internship in spatiotemporal adaptation of robots to humans in industrial assembly tasks

15 Feb 2024
Job Information

Organisation/Company

MINES ParisTech - Centre for Robotics

Research Field

Engineering » Electrical engineering

Researcher Profile

First Stage Researcher (R1)

Country

France

Application Deadline

30 Mar 2024 - 12:30 (Europe/Paris)

Type of Contract

Temporary

Job Status

Full-time

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Human action recognition plays a pivotal role in enabling seamless collaboration between humans and robots across various tasks and environments. By accurately detecting and interpreting human actions, robots can anticipate and respond to human intentions in real-time, facilitating efficient and intuitive interactions. Advanced computer vision techniques, such as deep learning algorithms, enable robots to analyze video data and recognize a wide range of human actions with high accuracy. This capability allows robots to understand complex gestures, movements, and behaviors executed by humans during collaborative activities. From manufacturing assembly lines to healthcare assistance and household chores, human action recognition equips robots with the intelligence to adapt their actions and provide timely assistance to human collaborators. As research continues to push the boundaries of human-robot interaction, further advancements in human action recognition promise to enhance the effectiveness and naturalness of collaborative efforts between humans and robots, ultimately leading to more productive and harmonious partnerships.

Robot energy consumption and human ergonomics are critical considerations in the design and operation of collaborative robotic systems. Efficient energy usage is essential to minimize operational costs, and reduce environmental impact. Therefore, robots are often designed with energy-efficient actuators, optimized control algorithms, and intelligent motion planning to minimize energy consumption while maintaining performance. On the other hand, human ergonomics focuses on ensuring that interactions with robots are safe, comfortable, and biomechanically sound for human operators. This involves designing ergonomic workstations, considering factors such as posture, reachability, and force exertion, to reduce the risk of musculoskeletal injuries and fatigue. Balancing robot energy consumption and human ergonomics is crucial for creating collaborative environments where both robots and humans can work efficiently and safely together. By optimizing both aspects, collaborative robotic systems can maximize productivity, enhance user satisfaction, and promote long-term health and well-being.

The objectives of the internship are:

1. Develop a robotic perception layer that recognizes human poses and actions in real-time and adjusts the robotic motion accordingly. Egocentric computer vision will be used to capture the professional actions and gestures of the operator. Human action recognition and pose estimation are parameters that the robot will consider for adapting its behavior.

2. Analysis, identification and measuring of both Robotic Factors (RF) and Human Factors (HF) that contribute in an optimised coefficiency of Human-Robot Collaboration.

The expected outcomes are:

1. Implementation of a fully operational Human-Robot Collaboration system for an industrial assembling task (e.g. TV assembly) using the Niryo robot.

2. Investigate the correlation between RF and HF with the goal of optimizing the coefficiency in Human-Robot Handovers

Requirements

Research Field

Computer science » Autonomic computing

Education Level

Master Degree or equivalent

Research Field

Engineering » Electrical engineering

Education Level

Bachelor Degree or equivalent

Skills/Qualifications

Required skills:

Electrical or Computer Engineering, or Computer Science University Degree, or MSc in Robotics, Applied Mathematics or Data Science or AI or similar with the above degrees. More precisely, the student should have very strong skills on:

• Machine and Deep Learning

• Computer Vision

• Programming: ROS, Python, C++, etc...

• The candidate must be proficient in both written and spoken English and possess excellent presentation and communication skills.

Languages

ENGLISH

Level

Excellent

Research Field

Computer science » Autonomic computingEngineering » Electrical engineering

Years of Research Experience

1 - 4

Additional Information
Selection process

How to apply or for further information:

Please send your CV and cover letter to [email protected] and [email protected]

Work Location(s)

Number of offers available

1

Company/Institute

MINES Paris

Country

France

City

Paris

Geofield

Where to apply

E-mail

[email protected]

Contact

City

Paris

Website

https://www.caor.minesparis.psl.eu

Street

60, Boulevard Saint-Michel

Postal Code

75006

STATUS: EXPIRED