text: 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.
  @cite_4: Human integration is essential in systems where autonomous control alone would not be successful. In this paper, we present a framework for integrating a human supervisor into an otherwise autonomous control system. To facilitate integration, discrete event systems theory is adopted to model human interactions. It is via this interaction model that human commands can be combined with commands from an automated control system. For control synthesis, a method based on constraints is being used to generate velocity commands from the autonomous task level controller. The constraints are also utilized to limit human input so that erroneous human input is minimized. The methods are demonstrated by experiments.
  @cite_7: Abstract The interaction between humans and robot teams is highly relevant in many application domains, for example in collaborative manufacturing, search and rescue, and logistics. It is well-known that humans and robots have complementary capabilities: Humans are excellent in reasoning and planning in unstructured environments, while robots are very good in performing tasks repetitively and precisely. In consequence, one of the key research questions is how to combine human and robot team decision making and task execution capabilities in order to exploit their complementary skills. From a controls perspective this question boils down to how control should be shared among them. This article surveys advances in human-robot team interaction with special attention devoted to control sharing methodologies. Additionally, aspects affecting the control sharing design, such as human behavior modeling, level of autonomy and human-machine interfaces are identified. Open problems and future research directions towards joint decision making and task execution in human-robot teams are discussed.
  @cite_19: This paper presents a new method for injecting human inputs into mixed-initiative interactions between humans and robots. The method is based on a model-predictive control (MPC) formulation, which inevitably involves predicting the system (robot dynamics as well as human input) into the future. These predictions are complicated by the fact that the human is interacting with the robot, causing the prediction method itself to have an effect on future human inputs. We investigate and develop different prediction schemes, including fixed and variable horizon MPCs and human input estimators of different orders. Through a search-and-rescue-inspired human operator study, we arrive at the conclusion that the simplest prediction methods outperform the more complex ones, i.e., in this particular case, less is indeed more.
  @cite_5: Tele-operated systems allow humans to extend their physical capabilities and enable them to intervene in hazardous operations or where their presence is not possible. However, the operation of such systems over long periods has proved to be difficult and stressful. Consequently, means to facilitate their use are the subject of much study and experimental work. In this paper, we propose a collaborative-shared control strategy that combines the operator abilities with robotic-based tasks to render these systems more flexible and robust. In our method, the collaborative control component is responsible for allowing operator intervention when the robot is facing complex situations, whilst the shared control component provides an automatic control mechanism to assist and to monitor-correct irrational operator actions. The paper demonstrates how collaborative and shared control strategies work together to facilitate the teleoperated control of a mobile platform in a cluttered environment. The experimental results include applications to surveillance and to search & rescue operations. In addition, a key component in the form of a hybrid obstacle avoidance module is introduced that allows the robot to be guided on a task basis by the operator at a distance.
  @cite_16: 
summary: The majority of related work in the shared control literature focuses on the development and analysis of techniques that statically and or dynamically allocate control between human and robot partners. The main objective in these works is to improve system performance while reducing the control burden on the human operator @cite_7 . In some applications, the autonomous partner is allocated the majority of the low-level control while the human operator acts in a supervisory role @cite_4 while in others, the roles are reversed, and the autonomous partner takes on the supervisory role @cite_5 . There is also related work in which neither partner acts as a supervisor and instead control is shared via a mixed-initiative paradigm. For example, researchers have developed techniques in which the autonomous partner is explicitly aware of the human operator's intention @cite_19 as well as techniques in which the autonomous partner has an implicit understanding of the human operator's control policy @cite_16 . While these examples aim to improve task performance, the main motivation for our work is to extend the human operator's control ability in domains where safety is a primary concern.