Learning shared control by demonstration for personalized wheelchair assistance

An emerging research problem in assistive robotics is the design of methodologies that allow robots to provide personalized assistance to users. For this purpose, we present a method to learn shared control policies from demonstrations offered by a human assistant. We train a Gaussian process (GP) regression model to continuously regulate the level of assistance between the user and the robot, given the user's previous and current actions and the state of the environment. The assistance policy is learned after only a single human demonstration, i.e. in one-shot. Our technique is evaluated in a one-of-a-kind experimental study, where the machine-learned shared control policy is compared to human assistance. Our analyses show that our technique is successful in emulating human shared control, by matching the location and amount of offered assistance on different trajectories. We observed that the effort requirement of the users were comparable between human-robot and human-human settings. Under the learned policy, the jerkiness of the user's joystick movements dropped significantly, despite a significant increase in the jerkiness of the robot assistant's commands. In terms of performance, even though the robotic assistance increased task completion time, the average distance to obstacles stayed in similar ranges to human assistance

One-shot assistance estimation from expert demonstrations for a shared control wheelchair system

An emerging research problem in the field of assistive robotics is the design of methodologies that allow robots to provide human-like assistance to the users. Especially within the rehabilitation domain, a grand challenge is to program a robot to mimic the operation of an occupational therapist, intervening with the user when necessary so as to improve the therapeutic power of the assistive robotic system. We propose a method to estimate assistance policies from expert demonstrations to present human-like intervention during navigation in a powered wheelchair setup. For this purpose, we constructed a setting, where a human offers assistance to the user over a haptic shared control system. The robot learns from human assistance demonstrations while the user is actively driving the wheelchair in an unconstrained environment. We train a Gaussian process regression model to learn assistance commands given past and current actions of the user and the state of the environment. The results indicate that the model can estimate human assistance after only a single demonstration, i.e. in one-shot, so that the robot can help the user by selecting the appropriate assistance in a human-like fashion

Haptic role allocation and intention negotiation in human-robot collaboration

This dissertation aims to present a perspective to build more natural shared control systems for physical human-robot cooperation. As the tasks become more complex and more dynamic, many shared control schemes fail to meet the expectation of an effortless interaction that resembles human-human sensory communication. Since such systems are mainly built to improve task performance, the richness of sensory communication is of secondary concern. We suggest that effective cooperation can be achieved when the human’s and the robot’s roles within the task are dynamically updated during the execution of the task. These roles define states for the system, in which the robot’s control leads or follows the human’s actions. In such a system, a state transition can occur at certain times if the robot can determine the user’s intention for gaining/relinquishing control. Specifically, with these state transitions we assign certain roles to the human and the robot. We believe that only by employing the robot with tools to change its behavior during collaboration, we can improve the collaboration experience. We explore how human-robot cooperation in virtual and physical worlds can be improved using a force-based role-exchange mechanism. Our findings indicate that the proposed role exchange framework is beneficial in a sense that it can improve task performance and the efficiency of the partners during the task, and decrease the energy requirement of the human. Moreover, the results imply that the subjective acceptability of the proposed model is attained only when role exchanges are performed in a smooth and transparent fashion. Finally, we illustrate that adding extra sensory cues on top of a role exchange scheme is useful for improving the sense of interaction during the task, as well as making the system more comfortable and easier to use, and the task more enjoyable

Modelling and animation of brittle fracture in three dimensions

This thesis describes a system for simulating fracture in brittle objects. The system combines rigid body simulation methods with a constraint-based model to animate fracturing of arbitrary polyhedral shaped objects under impact. The objects are represented as sets of masses, where pairs of adjacent masses are connected by a distance-preserving linear constraint. The movement of the objects are normally realized by unconstrained rigid body dynamics. The fracture calculations are only done at discrete collision events. In case of an impact, the forces acting on the constraints are calculated. These forces determine how and where the object will break. The problem with most of the existing fracture systems is that they only allow simulations to be done offline, either because the utilized techniques are computationally expensive or they require many small steps for accuracy. This work presents a near-real-time solution to the problem of brittle fracture and a graphical user interface to create realistic animations

Online Identification of Interaction Behaviors from Haptic Data during Collaborative Object Transfer

Joint object transfer is a complex task, which is less structured and less specific than what is existing in several industrial settings. When two humans are involved in such a task, they cooperate through different modalities to understand the interaction states during operation and mutually adapt to one another’s actions. Mutual adaptation implies that both partners can identify how well they collaborate (i.e. infer about the interaction state) and act accordingly. These interaction states can define whether the partners work in harmony, face conflicts, or remain passive during interaction. Understanding how two humans work together during physical interactions is important when exploring the ways a robotic assistant should operate under similar settings. This study acts as a first step to implement an automatic classification mechanism during ongoing collaboration to identify the interaction state during object co-manipulation. The classification is done on a dataset consisting of data from 40 subjects, who are partnered to form 20 dyads. The dyads experiment in a physical human-human interaction (pHHI) scenario to move an object in an haptics-enabled virtual environment to reach predefined goal configurations. In this study, we propose a sliding-window approach for feature extraction and demonstrate the online classification methodology to identify interaction patterns. We evaluate our approach using 1) a support vector machine classifier (SVMc) and 2) a Gaussian Process classifier (GPc) for multi-class classification, and achieve over 80% accuracy with both classifiers when identifying general interaction types

An animation system for fracturing of rigid objects

This paper describes a system for the animation of fracturing brittle objects. The system combines rigid body simulation methods with a constraint-based model to animate fracturing of arbitrary polyhedral shaped objects under impact. The objects are represented as sets of masses, where pairs of adjacent masses are connected via a distance-preserving linear constraint. Lagrange multipliers are used to compute the forces exerted by those constraints, where these forces determine how and where the object will break. However, a problem with existing systems is that the initial body models exhibit well-defined uniformity, which makes the generated animations unrealistic. This work introduces a method for generating more realistic cracks without any performance loss. This method is easy to implement and applicable on different models. Computer and Information Sciences - ISCIS 2005, 20th International Symposium, Istanbul, Turkey, October 26-28, 2005, Proceeding

Haptic negotiation and role exchange for collaboration in virtual environments

We investigate how collaborative guidance can be realized in multi-modal virtual environments for dynamic tasks involving motor control. Haptic guidance in our context can be defined as any form of force/tactile feedback that the computer generates to help a user execute a task in a faster, more accurate, and subjectively more pleasing fashion. In particular, we are interested in determining guidance mechanisms that best facilitate task performance and arouse a natural sense of collaboration. We suggest that a haptic guidance system can be further improved if it is supplemented with a role exchange mechanism, which allows the computer to adjust the forces it applies to the user in response to his/her actions. Recent work on collaboration and role exchange presented new perspectives on defining roles and interaction. However existing approaches mainly focus on relatively basic environments where the state of the system can be defined with a few parameters. We designed and implemented a complex and highly dynamic multimodal game for testing our interaction model. Since the state space of our application is complex, role exchange needs to be implemented carefully. We defined a novel negotiation process, which facilitates dynamic communication between the user and the computer, and realizes the exchange of roles using a three-state finite state machine. Our preliminary results indicate that even though the negotiation and role exchange mechanism we adopted does not improve performance in every evaluation criteria, it introduces a more personal and human-like interaction model

In-the-Wild Failures in a Long-Term HRI Deployment

Failures are typical in robotics deployments ``in-the-wild'', especially when robots perform their functions within social human spaces. This paper reports on the failures of an autonomous social robot called Lindsey, which has been used in a public museum for several years, covering over 1300 kilometres through its deployment. We present an analysis of distinctive failures observed during the deployment and focusing on those cases where the robot can leverage human help to resolve the problem situation. A final discussion outlines future research directions needed to ensure robots are equipped with adequate resources to detect and appropriately deal with failures requiring a human-in-the-loop approach

Conveying intentions through haptics in human-computer collaboration

Haptics has been used as a natural way for humans to communicate with computers in collaborative virtual environments. Human-computer collaboration is typically achieved by sharing control of the task between a human and a computer operator. An important research challenge in the field addresses the need to realize intention recognition and response, which involves a decision making process between the partners. In an earlier study, we implemented a dynamic role exchange mechanism, which realizes decision making by means of trading the parties' control levels on the task. This mechanism proved to show promise of a more intuitive and comfortable communication. Here, we extend our earlier work to further investigate the utility of a role exchange mechanism in dynamic collaboration tasks. An experiment with 30 participants was conducted to compare the utility of a role exchange mechanism with that of a shared control scheme where the human and the computer share control equally at all times. A no guidance condition is considered as a base case to present the benefits of these two guidance schemes more clearly. Our experiment show that the role exchange scheme maximizes the efficiency of the user, which is the ratio of the work done by the user within the task to the energy spent by her. Furthermore, we explored the added benefits of explicitly displaying the control state by embedding visual and vibrotactile sensory cues on top of the role exchange scheme. We observed that such cues decrease performance slightly, probably because they introduce an extra cognitive load, yet they improve the users' sense of collaboration and interaction with the computer. These cues also create a stronger sense of trust for the user towards her partner's control over the task

Don't Make the Same Mistakes Again and Again: Learning Local Recovery Policies for Navigation from Human Demonstrations

In this paper, we present a human-in-the-loop learning framework for mobile robots to generate effective local policies in order to recover from navigation failures in long-term autonomy. We present an analysis of failure and recovery cases derived from long-term autonomous operation of a mobile robot, and propose a two-layer learning framework that allows to detect and recover from such navigation failures. Employing a learning by demonstration (LbD) approach, our framework can incrementally learn to autonomously recover from situations it initially needs humans to help with. The learning framework allows for both real-time failure detection and regression using Gaussian processes (GPs). Our empirical results on two different failure scenarios indicate that given 40 failure state observations, the true positive rate of the failure detection model exceeds 90%, ending with successful recovery actions in more than 90% of all detected cases