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
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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. This paper provides the first comprehensive analysis of Uber's driver-partners, based on both survey data and anonymized, aggregated administrative data. Uber has grown at an exponential rate over the last few years, and drivers who partner with Uber appear to be attracted to the platform in large part because of the flexibility it offers, the level of compensation, and the fact that earnings per hour do not vary much with hours worked, which facilitates part-time and variable hours. Uber's driver-partners are more similar in terms of their age and education to the general workforce than to taxi drivers and chauffeurs. Uber may serve as a bridge for many seeking other employment opportunities, and it may attract well-qualified individuals because, with Uber's star rating system, driver-partners' reputations are explicitly shared with potential customers. Most of Uber's driver-partners had full- or part-time employment prior to joining Uber, and many continued in those positions after starting to drive with the Uber platform, which makes the flexibility to set their own hours all the more valuable. Uber's driver-partners also often cited the desire to smooth fluctuations in their income as a reason for partnering with Uber. We describe the design and evaluation of Chaski, a robot plan execution system that uses insights from human-human teaming to make human-robot teaming more natural and fluid. Chaski is a task-level executive that enables a robot to collaboratively execute a shared plan with a person. The system chooses and schedules the robot's actions, adapts to the human partner, and acts to minimize the human's idle time. We evaluate Chaski in human subject experiments in which a person works with a mobile and dexterous robot to collaboratively assemble structures using building blocks. We measure team performance outcomes for robots controlled by Chaski compared to robots that are verbally commanded, step-by-step by the human teammate. We show that Chaski reduces the human's idle time by 85 , a statistically significant difference. This result supports the hypothesis that human-robot team performance is improved when a robot emulates the effective coordination behaviors observed in human teams. In situated human-robot dialogue, although humans and robots are co-present in a shared environment, they have significantly mismatched capabilities in perceiving the shared environment. Their representations of the shared world are misaligned. In order for humans and robots to communicate with each other successfully using language, it is important for them to mediate such differences and to establish common ground. To address this issue, this paper describes a dialogue system that aims to mediate a shared perceptual basis during human-robot dialogue. In particular, we present an empirical study that examines the role of the robot’s collaborative effort and the performance of natural language processing modules in dialogue grounding. Our empirical results indicate that in situated human-robot dialogue, a low collaborative effort from the robot may lead its human partner to believe a common ground is established. However, such beliefs may not reflect true mutual understanding. To support truly grounded dialogues, the robot should make an extra effort by making its partner aware of its internal representation of the shared world. Several control systems in safety-critical applications involve the interaction of an autonomous controller with one or more human operators. Examples include pilots interacting with an autopilot system in an aircraft, and a driver interacting with automated driver-assistance features in an automobile. The correctness of such systems depends not only on the autonomous controller, but also on the actions of the human controller. In this paper, we present a formalism for human-in-the-loop (HuIL) control systems. Particularly, we focus on the problem of synthesizing a semi-autonomous controller from high-level temporal specifications that expect occasional human intervention for correct operation. We present an algorithm for this problem, and demonstrate its operation on problems related to driver assistance in automobiles. While human behavior prediction can increase the capability of a robotic partner to generate anticipatory behavior during physical human robot interaction (pHRI), predictions in uncertain situations can lead to large disturbances for the human if they do not match the human intentions. In this paper we present a novel control concept in which the assistive control parameters are adapted to the uncertainty in the sense that a the robot takes a more or less active role depending on its confidence in the human behavior prediction. The approach is based on risk-sensitive optimal feedback control. The human behavior is modeled using probabilistic learning methods and any unexpected disturbance is considered as a source of noise. The proposed approach is validated in situations with different uncertainties, process noise and risk-sensitivities in a tow- Degree-of-Freedom virtual reality experiment. Current applications of mobile robots in urban search and rescue (USAR) environments require a human operator in the loop to help guide the robot remotely. Although human operation can be effective, the unknown cluttered nature of the environments make robot navigation and victim identification highly challenging. Operators can become stressed and fatigued very quickly due to a loss of situational awareness, leading to the robots getting stuck and not being able to find victims in the scene during this time-sensitive operation. In addition, current autonomous robots are not capable of traversing these complex unpredictable environments. To address this challenge, a balance between the level of autonomy of the robot and the amount of human control over the robot needs to be addressed. In this paper, we present a unique control architecture for semi-autonomous navigation of a robotic platform utilizing sensory information provided by a novel real-time 3D mapping sensor. The control system provides the robot with the ability to learn and make decisions regarding which rescue tasks should be carried out at a given time and whether an autonomous robot or a human controlled robot can perform these tasks more efficiently without compromising the safety of the victims, rescue workers and the rescue robot. Preliminary experiments were conducted to evaluate the performance of the proposed collaborative control approach for a USAR robot in an unknown cluttered environment. We present a shared-control framework predicated on a measure of trust in the operator, that is calculated automatically based on the quality of the interactions between a human and autonomous system. This measure of trust is built upon a control-theoretic foundation that rewards stable operation of the system to give more trusted users additional control authority. The level of control authority is used to modify the human input, and as a result, we observe a minimization of the required effort of the controller. We validate this work within a planar crane environment with a receding horizon controller to assist with the regulation of the system dynamics. The human defines the reference trajectory for the controller. In an experimental study users navigate a suspended payload through a set of maze configurations. We find that adaptation of the trust metric over time provides the benefit of substantially ( @math ) improving the automated system's ability to modulate the user's input, resulting in stable reference trajectories that require less effort to track. In effect, the human and automation spend less time fighting each other during task execution, suggesting that the automated system and user each have a better understanding of the other's ability. Shared control is a key technology for various robotic applications in which a robotic system and a human operator are meant to collaborate efficiently. In order to achieve efficient task execution in shared control, it is essential to predict the desired behavior for a given situation or context in order to simplify the control task for the human operator. This prediction is obtained by exploiting Learning from Demonstration (LfD), which is a popular approach for transferring human skills to robots. We encode the demonstrated behavior as trajectory distributions and generalize the learned distributions to new situations. The goal of this paper is to present a shared control framework that uses learned expert distributions to gain more autonomy. Our approach controls the balance between the controller's autonomy and the human preference based on the distributions of the demonstrated trajectories. Moreover, the learned distributions are autonomously refined from collaborative task executions, resulting in a master-slave system with increasing autonomy that requires less user input with an increasing number of task executions. We experimentally validated that our shared control approach enables efficient task executions. Moreover, the conducted experiments demonstrated that the developed system improves its performances through interactive task executions with our shared control. Future applications for personal robots motivate research into developing robots that are intelligent in their interactions with people. Toward this goal, in this paper we present an integrated socio-cognitive architecture to endow an anthropomorphic robot with the ability to infer mental states such as beliefs, intents, and desires from the observable behavior of its human partner. The design of our architecture is informed by recent findings from neuroscience and embodies cognition that reveals how living systems leverage their physical and cognitive embodiment through simulation-theoretic mechanisms to infer the mental states of others. We assess the robot's mindreading skills on a suite of benchmark tasks where the robot interacts with a human partner in a cooperative scenario and a learning scenario. In addition, we have conducted human subjects experiments using the same task scenarios to assess human performance on these tasks and to compare the robot's performance with that of people. In the process, our human subject studies also reveal some interesting insights into human behavior. The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage and remains a major motivator for work in the field. We present a modular robot system capable of autonomously completing high-level tasks by reactively reconfiguring to meet the needs of a perceived, a priori unknown environment. The system integrates perception, high-level planning, and modular hardware and is validated in three hardware demonstrations. Given a high-level task specification, a modular robot autonomously explores an unknown environment, decides when and how to reconfigure, and manipulates objects to complete its task. The system architecture balances distributed mechanical elements with centralized perception, planning, and control. By providing an example of how a modular robot system can be designed to leverage reactive reconfigurability in unknown environments, we have begun to lay the groundwork for modular self-reconfigurable robots to address tasks in the real world. As robots begin to enter our homes and workplaces, they will have to deal with the devices and appliances that are already there. Unfortunately, devices that are easy for humans to operate often cause problems for robots [3]. In teleoperation settings, the lack of tactile feedback often makes manipulation of buttons and switches awkward and clumsy [7]. Also, the robot's gripper often occludes the control, making teleoperation difficult. In the autonomous setting, perception of small buttons and switches is often difficult due to sensor limitations and poor lighting conditions. Adding depth information does not help much, since many of the controls we want to manipulate are small, and often close to the noise threshold of currently-available depth sensors typically installed on a mobile robot. This makes it extremely difficult to segment the controls from the other parts of the device. In this paper, we present a shared autonomy approach to the operation of physical device controls. A human operator gives high-level guidance, helps identify controls and their locations, and sequences the actions of the robot. Autonomous software on our robot performs the lower-level actions that require closed-loop control, and estimates the exact positions and parameters of controls. We describe the overall system, and then give the results of our initial evaluations, which suggest that the system is effective in operating the controls on a physical device. An intelligent robot control scheme is presented which enables a cooperative work of humans and robots through direct contact interaction in a partially known environment. The control of the degrees of freedom of the manipulator is shared through a hybrid controller between the operator and the intelligent control system. The allocation of the control among human and robot occurs dynamically according to the state of the task execution and the capabilities of the human and the robot. Experiments were conducted to prove the effectiveness of our concept. Throughout the history of automation, there have been numerous accidents attributed to the inappropriate use of automated systems. Over-reliance and under-reliance on automation are well documented problems in fields where automation has been employed for a long time, such as factories and aviation. Research has shown that one of the key factors that influence an operator's reliance on automated systems is his or her trust of the system. Several factors, including risk, workload, and task difficulty have been found to influence an operator's trust of an automated system. With a model of trust based upon these factors, it is possible to design automated systems that foster well-calibrated trust and thereby prevent the misuse of automation. Over the past decade, robot systems have become more commonplace and increasingly autonomous. With the increased use of robot systems in multiple application domains, models of trust and operator behavior for human-robot interaction (HRI) must be created now in order to avoid some of the problems encountered by other automation domains in the past. Since traditional automation domains and HRI are significantly different, we reexamine trust and control allocation (operator's usage of autonomous behaviors) as it relates to robots with autonomous capabilities in order to discover the relevant factors in HRI. This dissertation examines existing work in traditional automation that is relevant to HRI and, based on that information, builds an experimental methodology to closely mimic real world remote robot teleoperation tasks. We also present results from multiple experiments examining the relationship between the different factors being investigated with respect to trust and control allocation. Based on these results, a model for human interaction with remote robots for teleoperation (HARRT) is proposed and design guidelines to help improve overall performance are presented based on the model. In this paper, we propose a role adaptation method for human-robot shared control. Game theory is employed for fundamental analysis of this two-agent system. An adaptation law is developed such that the robot is able to adjust its own role according to the human's intention to lead or follow, which is inferred through the measured interaction force. In the absence of human interaction forces, the adaptive scheme allows the robot to take the lead and complete the task by itself. On the other hand, when the human persistently exerts strong forces that signal an unambiguous intent to lead, the robot yields and becomes the follower. Additionally, the full spectrum of mixed roles between these extreme scenarios is afforded by continuous online update of the control that is shared between both agents. Theoretical analysis shows that the resulting shared control is optimal with respect to a two-agent coordination game. Experimental results illustrate better overall performance, in terms of both error and effort, compared with fixed-role interactions. This work extends the framework of bilateral shared control of mobile robots with the aim of increasing the robot autonomy and decreasing the operator commitment. We consider persistent autonomous behaviors where a cyclic motion must be executed by the robot. The human operator is in charge of modifying online some geometric properties of the desired path. This is then autonomously processed by the robot in order to produce an actual path guaranteeing: i) tracking feasibility, ii) collision avoidance with obstacles, iii) closeness to the desired path set by the human operator, and iv) proximity to some points of interest. A force feedback is implemented to inform the human operator of the global deformation of the path rather than using the classical mismatch between desired and executed motion commands. Physically-based simulations, with human hardware-in-the-loop and a quadrotor UAV as robotic platform, demonstrate the feasibility of the method.