We present a novel approach to shared control of human-machine systems. Our method assumes no a priori knowledge of the system dynamics. Instead, we learn both the dynamics and information about the user's interaction from observation through the use of the Koopman operator. Using the learned model, we define an optimization problem to compute the optimal policy for a given task, and compare the user input to the optimal input. We demonstrate the efficacy of our approach with a user study. We also analyze the individual nature of the learned models by comparing the effectiveness of our approach when the demonstration data comes from a user's own interactions, from the interactions of a group of users and from a domain expert. Positive results include statistically significant improvements on task metrics when comparing a user-only control paradigm with our shared control paradigm. Surprising results include findings that suggest that individualizing the model based on a user's own data does not effect the ability to learn a useful dynamic system. We explore this tension as it relates to developing human-in-the-loop systems further in the discussion.
Our motivation stems from a desire for a deeper understanding of how human-robot teams interact. In particular, we are interested in developing a methodology that allows us to dynamically adjust the amount of control authority given to the robot and human partners @cite_28 @cite_1 . If done intelligently, and with appropriate knowledge of the individual capabilities of each team member, we can improve the overall efficiency, stability and safety of the joint system. Approaches to shared control range from pre-defined, discretely adjustable methods @cite_15 to probabilistic models @cite_17 to policy blending @cite_12 . In addition to the original control signal space, shared control has been researched through haptic control @cite_5 and compliant control @cite_8 , and studied in numerous domains.
@cite_8 @cite_28 @cite_1 @cite_5 @cite_15 @cite_12 @cite_17
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Shared compliant control has been incorporated into an advanced six-degree-of-freedom force-reflecting telemanipulation system. With this system the authors have investigated the effect of time delay on human telemanipulation task performance. Time delays of between 2 and 4096 ms were introduced between master and slave arms, and high-precision peg-in-hole tasks were performed by six test operators with two modes of control: kinesthetic force feedback (KFF) and shared compliant control (SCC). Task performance was quantified in terms of the completion time (CT) and the sum of square forces (SOSF). The experimental results demonstrate the superiority of SCC over KFF for time-delayed telemanipulation. SCC has significantly lower rates of increase than KFF in both CT and SOSF with time delay. Only SCC enabled task performance at delays above 1 s. Constant force maintenance tasks were also performed to investigate the effect of time delay on the stability of force reflection. SCC also has beneficial effects on telemanipulation without time delay. > 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. We develop a new generalization of Koopman operator theory that incorporates the effects of inputs and control. Koopman spectral analysis is a theoretical tool for the analysis of nonlinear dynamical systems. Moreover, Koopman is intimately connected to Dynamic Mode Decomposition (DMD), a method that discovers spatial-temporal coherent modes from data, connects local-linear analysis to nonlinear operator theory, and importantly creates an equation-free architecture allowing investigation of complex systems. In actuated systems, standard Koopman analysis and DMD are incapable of producing input-output models; moreover, the dynamics and the modes will be corrupted by external forcing. Our new theoretical developments extend Koopman operator theory to allow for systems with nonlinear input-output characteristics. We show how this generalization is rigorously connected and generalizes a recent development called Dynamic Mode Decomposition with control (DMDc). We demonstrate this new theory on nonlinear dynamical systems, including a standard Susceptible-Infectious-Recovered model with relevance to the analysis of infectious disease data with mass vaccination (actuation). In this paper we present a method of shared control strategy for an intelligent wheelchair to assist a disable user in performing obstacle avoidance tasks. The system detects obstacles in front of the wheelchair using a laser range finder sensor. As the wheelchair moves the information from the laser range finder is combined with data from the encoders mounted in its driving wheels to build a 360° real-time map. The accuracy of the map is improved by eliminating the systematic error that would result from both the uncertainty of effective wheelbase and unequal driving wheel diameters. The usable wheelchair accessible space is determined by including the actual wheelchair dimensions in producing the real-time map. In making a decision the shared control method considers the user's intentions via the head-movement interface, accessible space of the environment and user safety. The experiments show promising results in the intelligent wheelchair system. 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.