Human-Robot Collaboration intro

Human-Robot Collaboration

We give humanoid robots the ability to help and collaborate with humans. 

Why Ver.3

Why

In modern societies, the demand for physical assistance to humans is increasing.  In factories, production workers execute repetitive tasks that, in the long run, often cause musculoskeletal diseases. In clinics, orthopedic patients need orthoses and prostheses to overcome their daily deficiencies.  At home, elderly people require a wide range of physical assistance to compensate for their muscles slowly loosing strength.  We thus need robot collaborators that  perceive humans and correct inefficient collaboration and unergonomic interaction that lead, in the long term, to musculoskeletal diseases.

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What

What

Robots can fulfill the human need for physical assistance. Traditional robots, however, are designed to act for humans. But the aforementioned general need of human-robot collaboration requires robots to act with humans in a shared workspace. To do so, robots that are nowadays proficient in physical interaction should become as proficient in physical collaboration. We need to develop safe dependable systems that can react, perceive and collaborate with human beings. To understand the biomechanics of human collaborative motion. To track, understand and predict human motion, in real-time, in dynamic environments. To integrate cognition technologies into human-robot collaboration. To develop tools for intuitive collaboration that increase human performance. 

To pursue the above objectives, what we do is to attempt at answering the following two research questions:

Q1: How can a robot help a human?

Q2: How can a human help a robot?

A fundamental concern here is to decode, from a mathematical perspective, what a human (or robot) help is. Some of our theoretical research efforts go along the direction of tackling and answering these open points and questions.

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How

How

The above objectives on human-robot collaboration are pursued by researching along with different directions.

Research on wearable sensors for force sensing

Research on wearable sensors for force sensing

We work on wearable sensors that allow us to measure the external forces of human beings. The external forces are then used to estimate the musculoskeletal stresses during specific tasks.

Research on wearable sensors for force sensing Details

Sandals with force/torque sensors

We have developed sandals equipped with homemade IIT force/torque sensors from which the interaction forces between the human feet and floor can be precisely measured.  Each force/torque sensor is also equipped with an IMU and two temperature sensors.

Sandals with tactile-sensor-based insole instead of force/torque sensors

To reduce the cost of a pair of sandals, it would be ideal to substitute the force-torque sensors with other cheaper sensors.  Hence, along with the iCub research line, we developed an insole able to measure the pressure distribution produced by the foot in contact with the sandal.  The insole is an array of capacity based tactile sensors, and the above video shows the activation of the sensor arrays after the human foot exerts pressure on the sandal.

Human-Robot collaboration Video gallery

Research on the on-line estimation of human musculoskeletal stresses

Research on the online estimation of human musculoskeletal stresses

The above wearable sensors are fundamental to retrieve the external forces acting on the human. These wearable sensors are then complemented with other wearable sensors to measure human motion. We use the Xsens wearable sensors to measure the position and orientation of the human limbs, and then we apply homemade IIT online estimation algorithms to retrieve the human posture.

By combining the human motion, the forces measured by the sensorised sandals, and the human model, we can also estimate the human musculoskeletal stresses. We have developed online Maximum a Posteriori (MAP) based algorithms that estimate the human musculoskeletal stresses in any human configuration.

The above video visualises the outcome of our estimation algorithms for human musculoskeletal stresses. More precisely, the human is performing some random motions and the robot stays still. Then, the whiter the circles around the human avatar on the right-hand side, the higher the estimated human musculoskeletal stresses. The yellow arrows at the avatar human feet represent the estimation of the forces between the human and the floor.

Research on the on-line estimation of human musculoskeletal stresses VIDEO

Research on the control of human-robot and physical interactions

Research on the control of human-robot and physical interactions

Using Lyapunov theory, this research attempts at answering the two aforementioned questions, i.e. 

Q1: How can a robot help a human?

Q2: How can a human help a robot?

Indeed, the kinematics (i.e. position and velocity) and dynamics (i.e. musculoskeletal stresses and foot forces) of the human being can then be sent to the robot that has to move for achieving coordinated human-robot actions. For instance, the robot can stand up from a chair while being helped by a human, and can also minimise some human musculoskeletal stress during this action.

The video below shows exactly this interaction, where the humanoid robot iCub stands up from a chair while being helped by a human being. The robot uses all the kinematic and dynamic information coming from the sensorised human.

Research on the control of human-robot and physical interactions VIDEO

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