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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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. 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 expectations and mutual understanding are critical facets of teamwork. Achieving these in human-robot collaborative contexts can be especially challenging, as humans and robots are unlikely to share a common language to convey intentions, plans, or justifications. Even in cases where human co-workers can inspect a robot's control code, and particularly when statistical methods are used to encode control policies, there is no guarantee that meaningful insights into a robot's behavior can be derived or that a human will be able to efficiently isolate the behaviors relevant to the interaction. We present a series of algorithms and an accompanying system that enables robots to autonomously synthesize policy descriptions and respond to both general and targeted queries by human collaborators. We demonstrate applicability to a variety of robot controller types including those that utilize conditional logic, tabular reinforcement learning, and deep reinforcement learning, synthesizing informative policy descriptions for collaborators and facilitating fault diagnosis by non-experts. In shared autonomy, user input and robot autonomy are combined to control a robot to achieve a goal. Often, the robot does not know a priori which goal the user wants to achieve, and must both predict the user's intended goal, and assist in achieving that goal. We formulate the problem of shared autonomy as a Partially Observable Markov Decision Process with uncertainty over the user's goal. We utilize maximum entropy inverse optimal control to estimate a distribution over the user's goal based on the history of inputs. Ideally, the robot assists the user by solving for an action which minimizes the expected cost-to-go for the (unknown) goal. As solving the POMDP to select the optimal action is intractable, we use hindsight optimization to approximate the solution. In a user study, we compare our method to a standard predict-then-blend approach. We find that our method enables users to accomplish tasks more quickly while utilizing less input. However, when asked to rate each system, users were mixed in their assessment, citing a tradeoff between maintaining control authority and accomplishing tasks quickly. 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. Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain---Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operators capabilities and their perception of control authority. Additionally, a custom servo controller design allow for safe interactions of the robotic arm with the environment. We present experimental results demonstrating significant performance improvement using our shared-control assistance framework on adapted rehabilitation benchmarks with two subjects at various timepoints relative to their implantation with intracortical BCIs. Our results indicate that shared assistance mitigates perceived user difficulty in using a seven-degree of freedom robotic arm as a prosthetic and enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with objects previously unknown to the system in densely cluttered environments. 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. We consider the problem of navigating a mobile robot through dense human crowds. We begin by exploring a fundamental impediment to classical motion planning algorithms called the âfreezing robot problemâ: once the environment surpasses a certain level of dynamic complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place or performs unnecessary maneuvers to avoid collisions. We argue that this problem can be avoided if the robot anticipates human cooperation, and accordingly we develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a âmultiple goalâ extension that models the goal-driven nature of human decision making. We validate this model with an empirical study of robot navigation in dense human crowds 488 runs, specifically testing how cooperation models effect navigation performance. The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 0.8 humans m2, while a state-of-the-art non-cooperative planner exhibits unsafe behavior more than three times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people m2. We also show that our non-cooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds. 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. Our human-robot collaboration research aims to improve the fluency and efficiency of interactions between humans and robots when executing a set of tasks in a shared workspace. During human-robot collaboration, a robot and a user must often complete a disjoint set of tasks that use an overlapping set of objects, without using the same object simultaneously. A key challenge is deciding what task the robot should perform next in order to facilitate fluent and efficient collaboration. Most prior work does so by first predicting the human's intended goal, and then selecting actions given that goal. However, it is often difficult, and sometimes impossible, to infer the human's exact goal in real time, and this serial predict-then-act method is not adaptive to changes in human goals. In this paper, we present a system for inferring a probability distribution over human goals, and producing assistance actions given that distribution in real time. The aim is to minimize the disruption caused by the nature of human-robot shared workspace. We extend recent work utilizing Partially Observable Markov Decision Processes (POMDPs) for shared autonomy in order to provide assistance without knowing the exact goal. We evaluate our system in a study with 28 participants, and show that our POMDP model outperforms state of the art predict-then-act models by producing fewer human-robot collisions and less human idling time. The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications. In this paper the problem of achieving a cooperative behavior in a dual-arm robot systemisaddressed.Acontrolstrategyisconceivedinwhichonerobotispositioncontrolledanddevoted to task execution, whereas a suitable compliance is conferred to the end effectoroftheotherrobottocopewithunavoidablemisalignmentbetweenthetwoarmsandtaskplanning inaccuracies. A modular control structure is adopted that allows the selectionof the proper operating mode for each robot, depending on the task requirements. Theproposed approach is experimentally tested in two different tasks involving the tworobots in the laboratory setup. First, a parts-mating task of peg-in-hole type is executed;the robot carrying the peg is position controlled, whereas the robot holding the hollowpart is controlled to behave as a mechanical impedance. Then, a pressure-forming taskis executed, in which a disk-shaped tool is required to align with a ﬂat surface whileexerting a desired pressure; in this case, the robot carrying the disk is position controlled,whereas the robot holding the surface is force controlled. In shared autonomy, user input is combined with semi-autonomous control to achieve a common goal. The goal is often unknown ex-ante, so prior work enables agents to infer the goal from user input and assist with the task. Such methods tend to assume some combination of knowledge of the dynamics of the environment, the user's policy given their goal, and the set of possible goals the user might target, which limits their application to real-world scenarios. We propose a deep reinforcement learning framework for model-free shared autonomy that lifts these assumptions. We use human-in-the-loop reinforcement learning with neural network function approximation to learn an end-to-end mapping from environmental observation and user input to agent action, with task reward as the only form of supervision. Controlled studies with users (n = 16) and synthetic pilots playing a video game and flying a real quadrotor demonstrate the ability of our algorithm to assist users with real-time control tasks in which the agent cannot directly access the user's private information through observations, but receives a reward signal and user input that both depend on the user's intent. The agent learns to assist the user without access to this private information, implicitly inferring it from the user's input. This allows the assisted user to complete the task more effectively than the user or an autonomous agent could on their own. This paper is a proof of concept that illustrates the potential for deep reinforcement learning to enable flexible and practical assistive systems. To enable safe and efficient human–robot collaboration in shared workspaces, it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative replanning with the above cost function. The key to our approach is thus to learn a cost function that “explains” the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use inverse optimal control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively replanning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer: stochastic trajectory optimizer for motion planning [1] ; it plans for a 23-degree-of-freedom human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace. Human-robot collaboration seeks to have humans and robots closely interacting in everyday situations. For some tasks, physical contact between the user and the robot may occur, originating significant challenges at safety, cognition, perception and control levels, among others. This paper focuses on robot motion adaptation to parameters of a collaborative task, extraction of the desired robot behavior, and variable impedance control for human-safe interaction. We propose to teach a robot cooperative behaviors from demonstrations, which are probabilistically encoded by a task-parametrized formulation of a Gaussian mixture model. Such encoding is later used for specifying both the desired state of the robot, and an optimal feedback control law that exploits the variability in position, velocity and force spaces observed during the demonstrations. The whole framework allows the robot to modify its movements as a function of parameters of the task, while showing different impedance behaviors. Tests were successfully carried out in a scenario where a 7 DOF backdrivable manipulator learns to cooperate with a human to transport an object. This paper studies how an operator with limited situational awareness can collaborate with a swarm of simulated robots. The robots are distributed in an environment with wall obstructions. They aggregate autonomously but are unable to form a single cluster due to the obstructions. The operator lacks the bird’s-eye perspective, but can interact with one robot at a time, and influence the behavior of other nearby robots. We conducted a series of experiments. They show that untrained participants had marginal influence on the performance of the swarm. Expert participants succeeded in aggregating 85 of the robots while untrained participants, with bird’s-eye view, succeeded in aggregating 90 . This demonstrates that the controls are sufficient for operators to aid the autonomous robots in the completion of the task and that lack of situational awareness is the main difficulty. An analysis of behavioral differences reveals that trained operators learned to gain superior situational awareness. We consider the cooperative control of a team of robots to estimate the position of a moving target using onboard sensing. In this setting, robots are required to estimate their positions using relative onboard sensing while concurrently tracking the target. Our probabilistic localization and control method takes into account the motion and sensing capabilities of the individual robots to minimize the expected future uncertainty of the target position. Two measures of uncertainty are extensively evaluated and compared: mutual information and the trace of the extended Kalman filter covariance. Our approach reasons about multiple possible sensing topologies and incorporates an efficient topology switching technique to generate locally optimal controls in polynomial time complexity. Simulations illustrate the performance of our approach and prove its flexibility in finding suitable sensing topologies depending on the limited sensing capabilities of the robots and the movements of the target. Furthermore, we demonstrate the applicability of our method in various experiments with single and multiple quadrotor robots tracking a ground vehicle in an indoor environment. Interest in human-robot coexistence, in which humans and robots share a common work volume, is increasing in manufacturing environments. Efficient work coordination requires both awareness of the human pose and a plan of action for both human and robot agents in order to compute robot motion trajectories that synchronize naturally with human motion. In this paper, we present a data-driven approach that synthesizes anticipatory knowledge of both human motions and subsequent action steps in order to predict in real-time the intended target of a human performing a reaching motion. Motion-level anticipatory models are constructed using multiple demonstrations of human reaching motions. We produce a library of motions from human demonstrations, based on a statistical representation of the degrees of freedom of the human arm, using time series analysis, wherein each time step is encoded as a multivariate Gaussian distribution. We demonstrate the benefits of this approach through offline statistical analysis of human motion data. The results indicate a considerable improvement over prior techniques in early prediction, achieving 70 or higher correct classification on average for the first third of the trajectory (< 500msec). We also indicate proof-of-concept through the demonstration of a human-robot cooperative manipulation task performed with a PR2 robot. Finally, we analyze the quality of task-level anticipatory knowledge required to improve prediction performance early in the human motion trajectory. Directing robot attention to recognise activities and to anticipate events like goal-directed actions is a crucial skill for human-robot interaction. Unfortunately, issues like intrinsic time constraints, the spatially distributed nature of the entailed information sources, and the existence of a multitude of unobservable states affecting the system, like latent intentions, have long rendered achievement of such skills a rather elusive goal. The problem tests the limits of current attention control systems. It requires an integrated solution for tracking, exploration and recognition, which traditionally have been seen as separate problems in active vision. We propose a probabilistic generative framework based on information gain maximisation and a mixture of Kalman Filters that uses predictions in both recognition and attention-control. This framework can efficiently use the observations of one element in a dynamic environment to provide information on other elements, and consequently enables guided exploration. Interestingly, the sensors control policy, directly derived from first principles, represents the intuitive trade-off between finding the most discriminative clues and maintaining overall awareness. Experiments on a simulated humanoid robot observing a human executing goal-oriented actions demonstrated improvement on recognition time and precision over baseline systems. In shared control teleoperation, the robot assists the user in accomplishing the desired task, making teleoperation easier and more seamless. Rather than simply executing the user's input, which is hindered by the inadequacies of the interface, the robot attempts to predict the user's intent, and assists in accomplishing it. In this work, we are interested in the scientific underpinnings of assistance: we propose an intuitive formalism that captures assistance as policy blending, illustrate how some of the existing techniques for shared control instantiate it, and provide a principled analysis of its main components: prediction of user intent and its arbitration with the user input. We define the prediction problem, with foundations in inverse reinforcement learning, discuss simplifying assumptions that make it tractable, and test these on data from users teleoperating a robotic manipulator. We define the arbitration problem from a control-theoretic perspective, and turn our attention to what users consider good arbitration. We conduct a user study that analyzes the effect of different factors on the performance of assistance, indicating that arbitration should be contextual: it should depend on the robot's confidence in itself and in the user, and even the particulars of the user. Based on the study, we discuss challenges and opportunities that a robot sharing the control with the user might face: adaptation to the context and the user, legibility of behavior, and the closed loop between prediction and user behavior.