We describe a shared control methodology that can, without knowledge of the task, be used to improve a human's control of a dynamic system, be used as a training mechanism, and be used in conjunction with Imitation Learning to generate autonomous policies that recreate novel behaviors. Our algorithm introduces autonomy that assists the human partner by enforcing safety and stability constraints. The autonomous agent has no a priori knowledge of the desired task and therefore only adds control information when there is concern for the safety of the system. We evaluate the efficacy of our approach with a human subjects study consisting of 20 participants. We find that our shared control algorithm significantly improves the rate at which users are able to successfully execute novel behaviors. Experimental results suggest that the benefits of our safety-aware shared control algorithm also extend to the human partner's understanding of the system and their control skill. Finally, we demonstrate how a combination of our safety-aware shared control algorithm and Imitation Learning can be used to autonomously recreate the demonstrated behaviors.
For this reason, the most closely related shared control paradigms are safety, or context, aware. In this area, researchers have explored the impact of autonomous obstacle avoidance on teleoperated robots in search and rescue @cite_5 . Additionally, safety is a particular concern when the human and robot partner are co-located, such as with autonomous vehicles @cite_24 . Co-located partners are also common in assistive robotics where researchers have developed environment-aware smart walkers @cite_8 to help visually-impaired people avoid dynamic obstacles.
@cite_24 @cite_5 @cite_8
1985574396 1568126255 2061118406
Collaboration between heterogeneous robots can greatly improve the overall robot system by obtaining capabilities that each single robot is unable to achieve. In this paper, we present a collaborative robot system designed for search and rescue missions in an unknown environment with obstacles. The system consists of an aerial robot and a ground robot. An extended Kalman filter (EKF) is used for robot pose estimation, and an online trajectory generation algorithm is implemented for dynamic obstacle avoidance of the ground robot. The aerial robot first surveys an area of interests and sources a number of targets. The ground robot is then guided by the aerial robot to reach the target location while at the same time avoids obstacles along the way using a laser range finder. The system is entirely autonomous, achieves maximum efficiency and releases the human operator from all low-level types of operations. A centralized EKF is implemented with the flexibility of easily being modified into a distributed EKF. A major goal in robot motion planning and control is to be able to specify a task in a high-level, expressive language and have the robot(s) to automatically convert the specification into a set of low-level primitives, such as feedback controllers and communication protocols, to accomplish the task. The robots can vary from manipulator arms used in manufacturing or surgery, to autonomous vehicles used in search and rescue or in planetary exploration, and to smart wheel chairs for disabled people. They are subject to mechanical constraints (e.g., a carlike robot cannot move sideways,an airplane cannot stop in place) and have limited computation, sensing, and communication capabilities. The environments can be cluttered with possibly moving and shape-changing obstacles and can con tain dynamic (moving, appearing, or disappearing) targets. One of the major challenges in this area is the development of a computationally efficient frame work accommodating both the robot constraints and the complexity of the environment, while, at the same time, allowing for a large spectrum of task specifications. This paper presents a new adaptive servo-level shared control scheme for a mobile assistive robot that aims at assisting senior and disabled people to transport heavy objects in a complex environment with obstacles. Several technical problems and challenges related to the assistive robotic system and the shared controller are addressed. Specifically, a nonlinear tracking controller is developed for the robot to follow the user. An obstacle avoidance controller is developed based on the deformable virtual zone principle for the robot to avoid obstacles. The adaptive servo-level shared controller utilizes the tracking controller and the obstacle avoidance controllers outputs to generate a new shared control output to command the robot. Experiments show that the user can guide the movement of the robot safely and smoothly in the complex environment with the developed controllers.