Physically demanding labor is required of workers in construction and logistics, as well as dangerous jobs such as disaster relief, firefighting, policework, logging, and mining. Many of these workers in the US are injured, disabled, or die due to workplace incidents. Although robots have the potential to protect the well-being of these workers, they do not yet possess the capability to perform the demanding physical tasks which are inherent to these activities. A key missing aspect is the intelligence to intuitively coordinate their body to amplify the forces that they apply to objects, such as when humans naturally lean against a heavy door to open it. This project focuses on developing the technology to combine the control intelligence of humans with the physical strength and endurance of machines. In this framework, a wearable device measures the posture of the entire body of a human operator such that the remote human-like robot may reproduce the same whole-body position at the same time. The novelty of this project is to enable the human operator to simultaneously feel the forces that the robot applies to its surroundings. For instance, a firefighter commands the robot to push heavy debris by performing the motion herself, while simultaneously feeling whether the robot is exerting insufficient force and the object isn’t moving, or whether it is exerting excessive effort and losing balance. The impact of this research is the protection of the well-being of human workers by taking them away from danger, while leveraging their domain expertise and their motor intelligence to perform physically demanding labor. This research promotes the inclusion of undergraduate and underrepresented minorities in research, creates outreach activities with K-12 students, and proposes demonstrations of the system during conferences and University events.
This project explores a novel bilateral teleoperation framework which enables a human operator to control a remote wheeled humanoid robot to perform Dynamic Mobile Manipulation (DMM). DMM are physical tasks which combine forceful manipulation and agile locomotion. The key hypothesis in this proposal is that bilateral teleoperation via a whole-body haptic device can achieve safe and intuitive DMM in unstructured field environments. To validate this hypothesis, three specific aims will be pursued over three years. Aim 1: Implement the hardware that enables this research, composed of (i) a prototype whole-body haptic device which reads the operator’s posture to control the remote robot, and provides rich kinesthetic, visual, and audial feedback from the robot’s interaction with its environment, and (ii) the robot named SATYRR, which combines advantages of legged systems, wheeled locomotion, and an anthropomorphic upper-body for dual-arm manipulation. Aim 2: Study whole-body bilateral teleoperation strategies for DMM. Devise a human-to-robot motion mapping for intuitive, high-bandwidth control, and implement force feedback schemes that deliver interpretable feedback to the operator even under large impacts. And Aim 3: Improve DMM safety using shared autonomy. Devise model predictive control schemes to follow the user's input as closely as possible while preventing operator inputs that would damage the robot. To evaluate the planned framework, a realistic laboratory task environment will be built with an obstacle course that includes pushing large objects, opening blocked doors, balancing under external disturbances, and carrying and throwing a payload.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
