Posture Optimization in p-HRI and Teleoperation

Abstract

Autonomous and intelligent postural optimization in p-HRI and teleoperation is a fresh trend in the robotics research. Researchers have tried different type of gradient-based and gradient-free optimization approaches to find the optimal postural correction of the human interacting with a robot in some sparse set of tasks.
In our reserach, we had a fundamental view over the problem. We categorized different types of p-HRI and teleoperation tasks and proposed the first general framework for postural optimization in p-HRI and teleoperation.

We benefit from DULA and DEBA ergonomics models from our other work in gradient-based postural optimizations and we show the effect of postural optimization on reducing the risk of injuries in a simulation-based study.

We are also planning to conduct a human subject study to evaluate our approach on the behavior or real humans.

Postural Optimization in Co-Manipulation

In co-manipulation, the robot helps a human to move an object from an initial pose to the desired pose. There is no specific trajectory for the object to follow; the human plans and leads the motion, and the robot follows and provides help carrying the object. Meanwhile, the ergonomically intelligent p-HRI seeks to find the optimal posture for the human at each instance while holding the pose and velocities at the interaction point close to the current state. Once found, the system will suggest the solution to the human. We call this online postural optimization and we formalize finding the ergonomically optimal posture with the following optimization problem:

It is important to note that the optimal posture from the above equation for each time step is then suggested to the human to move towards. The human can refuse or accept and try to apply it as much as possible while completing the task.

Postural Optimization in Teleoperation

The main distinction of teleoperation from other p-HRI applications is the motion (and force) coupling between the leader and follower robots. The motion coupling between the two robots is not necessarily a one-to-one scale, and the coupling can be paused and resumed. This enables the teleoperator to disengage the leader robot in the middle of a task in many types of application, reposition their postures (and the leader robot as the result), and resume the teleoperation from a a more comfortable posture. The relative position trajectory of the follower robot remains unchanged; however, its velocity trajectory changes due to the pause. The motion scaling also helps keep the teleoperator’s posture in a smaller comfort zone while the follower robot operates in a much wider zone. To use this feature of teleoperation in postural optimization, we define three types of postural optimization, customized for different types of teleoperation that are shown below.

Online Postural Optimization in Teleoperation

Online postural optimization in teleoperation is very similar to online postural optimization in co-manipulation. The difference is that instead of having initial and goal poses for the shared object in co-manipulation, in teleoperation, we have initial and goal poses for the end-effector of the follower robot (or the object it manipulates). Hence, the ergonomically intelligent system should find the optimal posture of the human at each instance, in which the pose and velocities of the follower robot’s end-effector are not far from the current states, and suggest it to the human.

Initial Postural Optimization in Teleoperation

The remote connection between the leader and follower robots makes it possible to start the teleoperation task from any initial human posture and the corresponding leader robot’s initial posture and perform the same task with the follower robot, considering the human and leader robot’s workspace. We use this feature to propose initial postural optimization for path-constrained and trajectory-constrained teleoperation tasks. In this type of postural optimization, the ergonomically intelligent system observes the human doing the task once, then calculates the optimal initial posture for the human to start the same task with the same task-space motion for the follower robot. It also requires recording the entire motion of the robots and the human during the task.

Postural Optimization by Interface Reconfiguration in Teleoperation

An inherent feature of path-constrained teleoperation is the ability to pause the teleoperation by disengaging the leader and follower robots from each other, move the leader robot to a new position, and resume the teleoperation. This is usually done by a clutch switch placed under the user’s foot. Benefiting from this feature, we propose postural optimization by interface reconfiguration, in which, when the ergonomically intelligent system detects high-risk postures during a previously recorded task, suggests to pause the teleoperation. When teleoperation is paused, the system calculates a new optimal posture for the human to resume the teleoperation from it and continue the task.

We solve the above optimizations using sequential quadratic priogramming as a type of gradient-based optimization methods using DULA as the objectivbe function.

Results

Comparint with the gradient-free optimization for a simple teleoperation tasks, we see that gradient-based approach using DULA performs better and faster.

Related Publications

1. "Ergonomically Intelligent Physical Human-Robot Interaction: Postural Estimation, Assessment, and Optimization", Amir Yazdani, Roya Sabbagh Novin, Andrew Merryweather, Tucker Hermans, submitted to AAAI AI-HRI, 2021. arXiv

2. "DULA: A Differentiable Ergonomics Model for Postural Optimization in Physical HRI", Amir Yazdani, Roya Sabbagh Novin, Andrew Merryweather, Tucker Hermans, RSS workshop Robotics for People, 2021, arXiv

3. "Posture Estimation and Optimization in Ergonomically Intelligent Teleoperation Systems", Amir Yazdani, Roya Sabbagh Novin, Companion of the ACM/IEEE HRI, 2021, PDF