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.
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.
@cite_4 @cite_7 @cite_19 @cite_5 @cite_16
2119868928 2766053765 2118013858 1568126255 2734800126
In human-computer collaboration involving haptics, a key issue that remains to be solved is to establish an intuitive communication between the partners. Even though computers are widely used to aid human operators in teleoperation, guidance, and training, because they lack the adaptability, versatility, and awareness of a human, their ability to improve efficiency and effectiveness in dynamic tasks is limited. We suggest that the communication between a human and a computer can be improved if it involves a decision-making process in which the computer is programmed to infer the intentions of the human operator and dynamically adjust the control levels of the interacting parties to facilitate a more intuitive interaction setup. In this paper, we investigate the utility of such a dynamic role exchange mechanism, where partners negotiate through the haptic channel to trade their control levels on a collaborative task. We examine the energy consumption, the work done on the manipulated object, and the joint efficiency in addition to the task performance. We show that when compared to an equal control condition, a role exchange mechanism improves task performance and the joint efficiency of the partners. We also show that augmenting the system with additional informative visual and vibrotactile cues, which are used to display the state of interaction, allows the users to become aware of the underlying role exchange mechanism and utilize it in favor of the task. These cues also improve the user's sense of interaction and reinforce his her belief that the computer aids with the execution of the task. Collaborative robots represent a clear added value to manufacturing, as they promise to increase productivity and improve working conditions of such environments. Although modern robotic systems have become safe and reliable enough to operate close to human workers on a day-to-day basis, the workload is still skewed in favor of a limited contribution from the robot's side, and a significant cognitive load is allotted to the human. We believe the transition from robots as recipients of human instruction to robots as capable collaborators hinges around the implementation of transparent systems, where mental models about the task are shared between peers, and the human partner is freed from the responsibility of taking care of both actors. In this work, we implement a transparent task planner able to be deployed in realistic, near-future applications. The proposed framework is capable of basic reasoning capabilities for what concerns role assignment and task allocation, and it interfaces with the human partner at the level of abstraction he is most comfortable with. The system is readily available to non-expert users, and programmable with high-level commands in an intuitive interface. Our results demonstrate an overall improvement in terms of completion time, as well as a reduced cognitive load for the human partner. In this paper, adaptive impedance control is proposed for a robot collaborating with a human partner, in the presence of unknown motion intention of the human partner and unknown robot dynamics. Human motion intention is defined as the desired trajectory in the limb model of the human partner, which is extremely difficult to obtain considering the nonlinear and time-varying property of the limb model. Neural networks are employed to cope with this problem, based on which an online estimation method is developed. The estimated motion intention is integrated into the developed adaptive impedance control, which makes the robot follow a given target impedance model. Under the proposed method, the robot is able to actively collaborate with its human partner, which is verified through experiment studies. Efficient collaboration requires collaborators to monitor the behaviors of their partners, make inferences about their task intent, and plan their own actions accordingly. To work seamlessly and efficiently with their human counterparts, robots must similarly rely on predictions of their users' intent in planning their actions. In this paper, we present an anticipatory control method that enables robots to proactively perform task actions based on anticipated actions of their human partners. We implemented this method into a robot system that monitored its user's gaze, predicted his or her task intent based on observed gaze patterns, and performed anticipatory task actions accord- ing to its predictions. Results from a human-robot interaction experiment showed that anticipatory control enabled the robot to respond to user requests and complete the task faster-2.5 seconds on average and up to 3.4 seconds-compared to a robot using a reactive control method that did not anticipate user intent. Our findings highlight the promise of performing anticipatory actions for achieving efficient human-robot teamwork. The goal of this work is to develop computational models of social intelligence that enable robots to work side by side with humans, solving problems and achieving task goals through dialogue and collaborative manipulation. A defining problem of collaborative behavior in an embodied setting is the manner in which multiple agents make use of shared resources. In a situated dialogue, these resources include physical bottlenecks such as objects or spatial regions, and cognitive bottlenecks such as the speaking floor. For a robot to function as an effective collaborative partner with a human, it must be able to seize and yield such resources appropriately according to social expectations. We describe a general framework that uses timed Petri nets for the modeling and execution of robot speech, gaze, gesture, and manipulation for collaboration. The system dynamically monitors resource requirements and availability to control real-time turn-taking decisions over resources that are shared with humans, reasoning about different resource types independently. We evaluate our approach with an experiment in which our robot Simon performs a collaborative assembly task with 26 different human partners, showing that the multimodal reciprocal approach results in superior task performance, fluency, and balance of control.