Value of Information in Feedback Control

In this article, we investigate the impact of information on networked control systems, and illustrate how to quantify a fundamental property of stochastic processes that can enrich our understanding about such systems. To that end, we develop a theoretical framework for the joint design of an event trigger and a controller in optimal event-triggered control. We cover two distinct information patterns: perfect information and imperfect information. In both cases, observations are available at the event trigger instantly, but are transmitted to the controller sporadically with one-step delay. For each information pattern, we characterize the optimal triggering policy and optimal control policy such that the corresponding policy profile represents a Nash equilibrium. Accordingly, we quantify the value of information $\operatorname{VoI}_k$ as the variation in the cost-to-go of the system given an observation at time $k$. Finally, we provide an algorithm for approximation of the value of information, and synthesize a closed-form suboptimal triggering policy with a performance guarantee that can readily be implemented

Perception of delay in haptic telepresence systems

Time delay is recognized as an important issue in haptic telepresence systems as it is inherent to long-distance data transmission. What factors influence haptic delay perception in a time-delayed environment are, however, largely unknown. In this article, we examine the impact of manual movement frequency and amplitude in a sinusoidal exploratory movement as well as the stiffness of the haptic environment on the detection threshold for delay in haptic feedback. The results suggest that the detection of delay in force feedback depends on the movement frequency and amplitude, while variation of the absolute feedback force level does not influence the detection threshold. A model based on the exploration movement is proposed and guidelines for system design with respect to the time delay in haptic feedback are provided

On Composite Quantum Hypothesis Testing

We extend quantum Stein's lemma in asymmetric quantum hypothesis testing to composite null and alternative hypotheses. As our main result, we show that the asymptotic error exponent for testing convex combinations of quantum states ρ^(⊗n) against convex combinations of quantum states σ^(⊗n) is given by a regularized quantum relative entropy distance formula. We prove that in general such a regularization is needed but also discuss various settings where our formula as well as extensions thereof become single-letter. This includes a novel operational interpretation of the relative entropy of coherence in terms of hypothesis testing. For our proof, we start from the composite Stein's lemma for classical probability distributions and lift the result to the non-commutative setting by only using elementary properties of quantum entropy. Finally, our findings also imply an improved Markov type lower bound on the quantum conditional mutual information in terms of the regularized quantum relative entropy - featuring an explicit and universal recovery map

Distributed controller design for a class of sparse singular systems with privacy constraints

In the current research on distributed control of interconnected large-scale dynamical systems an often neglected issue is the desire to ensure privacy of subsystems. This gives motivation for the presented distributed controller design method which requires communication and the exchange of model data only with direct neighbors. Thus, no global system knowledge is required. An important property of many large-scale systems is the presence of algebraic conservation constraints, for example in terms of energy or mass flow. Therefore, the presented controller design takes these constraints explicitly into account while preserving the sparsity structure of the distributed system necessary for a distributed design. The computation is based on the simulation of the system states and of adjoint states. The control objective is represented by the finite horizon linear quadratic cost functional

Hybrid robotic and electrical stimulation assistance can enhance performance and reduce mental demand

Combining functional electrical stimulation (FES) and robotics may enhance recovery after stroke, by providing neural feedback with the former while improv- ing quality of motion and minimizing muscular fatigue with the latter. Here, we explored whether and how FES, robot assistance and their combination, affect users’ per- formance, effort, fatigue and user experience. 15 healthy participants performed a wrist flexion/extension tracking task with FES and/or robotic assistance. Tracking per- formance improved during the hybrid FES-robot and the robot-only assistance conditions in comparison to no assistance, but no improvement is observed when only FES is used. Fatigue, muscular and voluntary effort are estimated from electromyographic recording. Total muscle contraction and volitional activity are lowest with robotic assistance, whereas fatigue level do not change between the conditions. The NASA-Task Load Index answers indi- cate that participants found the task less mentally demand- ing during the hybrid and robot conditions than the FES condition. The addition of robotic assistance to FES train- ing might thus facilitate an increased user engagement compared to robot training and allow longer motor training session than with FES assistance

Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception.

When moving a piano or dancing tango with a partner, how should I control my arm muscles to sense their movements and follow or guide them smoothly? Here we observe how physically connected pairs tracking a moving target with the arm modify muscle coactivation with their visual acuity and the partner's performance. They coactivate muscles to stiffen the arm when the partner's performance is worse and relax with blurry visual feedback. Computational modeling shows that this adaptive sensing property cannot be explained by the minimization of movement error hypothesis that has previously explained adaptation in dynamic environments. Instead, individuals skillfully control the stiffness to guide the arm toward the planned motion while minimizing effort and extracting useful information from the partner's movement. The central nervous system regulates muscle activation to guide motion with accurate task information from vision and haptics while minimizing the metabolic cost. As a consequence, the partner with the most accurate target information leads the movement.NEW & NOTEWORTHY Our results reveal that interacting humans inconspicuously modulate muscle activation to extract accurate information about the common target while considering their own and the partner's sensorimotor noise. A novel computational model was developed to decipher the underlying mechanism: muscle coactivation is adapted to combine haptic information from the interaction with the partner and own visual information in a stochastically optimal manner. This improves the prediction of the target position with minimal metabolic cost in each partner, resulting in the lead of the partner with the most accurate visual information

Human-Robot Team Interaction Through Wearable Haptics for Cooperative Manipulation

The interaction of robot teams and single human in teleoperation scenarios is beneficial in cooperative tasks, for example the manipulation of heavy and large objects in remote or dangerous environments. The main control challenge of the interaction is its asymmetry, arising because robot teams have a relatively high number of controllable degrees of freedom compared to the human operator. Therefore, we propose a control scheme that establishes the interaction on spaces of reduced dimensionality taking into account the low number of human command and feedback signals imposed by haptic devices. We evaluate the suitability of wearable haptic fingertip devices for multi-contact teleoperation in a user study. The results show that the proposed control approach is appropriate for human-robot team interaction and that the wearable haptic fingertip devices provide suitable assistance in cooperative manipulation tasks