A Framework of Hybrid Force/Motion Skills Learning for Robots

Human factors and human-centred design philosophy are highly desired in today’s robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase usability and acceptability of robots by the users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm, contact force with the environment also play important roles in understanding and generating human-like manipulation behaviours for robots, e.g., in physical HRI and tele-operation. To this end, we present a novel robot learning framework based on Dynamic Movement Primitives (DMPs), taking into consideration both the positional and the contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter Robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table

Towards virtual communities on the Web: Actors and audience

We report about ongoing research in a virtual reality environment where visitors can interact with agents that help them to obtain information, to perform certain transactions and to collaborate with them in order to get some tasks done. Our environment models a theatre in our hometown. We discuss attempts to let this environment evolve into a theatre community where we do not only have goal-directed visitors, but also visitors that that are not sure whether they want to buy or just want information or visitors who just want to look around. It is shown that we need a multi-user and multiagent environment to realize our goals. Since our environment models a theatre it is also interesting to investigate the roles of performers and audience in this environment. For that reason we discuss capabilities and personalities of agents. Some notes on the historical development of networked communities are included

Psychologically based Virtual-Suspect for Interrogative Interview Training

In this paper, we present a Virtual-Suspect system which can be used to train inexperienced law enforcement personnel in interrogation strategies. The system supports different scenario configurations based on historical data. The responses presented by the Virtual-Suspect are selected based on the psychological state of the suspect, which can be configured as well. Furthermore, each interrogator's statement affects the Virtual-Suspect's current psychological state, which may lead the interrogation in different directions. In addition, the model takes into account the context in which the statements are made. Experiments with 24 subjects demonstrate that the Virtual-Suspect's behavior is similar to that of a human who plays the role of the suspect

A methodology for maintaining trust in virtual environments

The increasing interest in carrying out business in virtual environments has resulted in much research and discussion of trust establishment between the entities involved. Researchers over the years have acknowledged that the success of any transaction or interaction via the virtual medium is determined by the trust level between trusting agent and trusted agent. Numerous publications have attempted to address the various challenges of assigning a trust level and building trust in an interacting party. However, the building and allocating a value of trust is neither easy nor quick. It involves high cost and effort. Hence, the ensuing research challenge is how to maintain the trust that has been established and assigned. Due to the dynamic nature of trust, the trust evolution, and the fragility of trust in virtual environments, one of the most pressing challenges facing the research community is how trust can be maintained over time. This thesis is an effort in that direction. Specifically, the objective of this thesis is to propose a methodology for trust maintenance in virtual environments which we term “Trust Maintenance Methodology” (TMM). The methodology comprises five frameworks that can be used to achieve the objective of trust maintenance.In order to achieve the aforesaid objective, this thesis proposes a: (a) Framework for third party agent selection, (b) Framework for Formalization and Negotiation of service requirements, (c) Framework for Proactive Continuous Performance Monitoring, (d) Framework for Incentive Mechanism, and (e) Framework for Trust Re-calibration.The framework for third party agent selection is used for choosing and selecting a neutral agent who will supervise the interaction between two parties. This is the first step of our methodology. The neutral agent is involved throughout the course of the interaction between two parties and takes a proactive-corrective role in continuous performance monitoring. Once both parties have chosen a neutral agent, they carry out a formalization and negotiation process of their service requirements using our proposed framework. This is in order to create an SLA which will guide the interaction between two parties. The framework for proactive continuous performance monitoring then can be used to evaluate the performance of both parties in delivering their service based on the SLA. If a performance gap occurs during the course of transaction, the third party agent will take action to help both parties close the performance gap in a timely manner. A key salient feature of our continuous performance monitoring is that it is proactive-corrective. Additionally, we design a framework for providing an incentive during the course of interaction to motivate both parties to perform as closely as possible to the terms of the mutual agreement or SLA. By the end of the interaction time space, both parties will be able to re-assess or re-calibrate their trust level using our proposed framework for trust re-calibration.Finally, in order to validate our proposed methodology, we engineered a multi-agent system to simulate the validity of the TMM. Numerous case studies are presented to elucidate the workings of our proposed methodology. Moreover, we run several experiments under various testing conditions including boundary conditions. The results of experiments show that our methodology is effective in assisting the parties to maintain their trust level in virtual environments

Role Playing Learning for Socially Concomitant Mobile Robot Navigation

In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, we call this process Role Playing Learning, which is formulated under a reinforcement learning (RL) framework. The NN policy is optimized end-to-end using Trust Region Policy Optimization (TRPO), with consideration of the imperfectness of robot's sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of our method

The mechanics of trust: a framework for research and design

With an increasing number of technologies supporting transactions over distance and replacing traditional forms of interaction, designing for trust in mediated interactions has become a key concern for researchers in human computer interaction (HCI). While much of this research focuses on increasing users’ trust, we present a framework that shifts the perspective towards factors that support trustworthy behavior. In a second step, we analyze how the presence of these factors can be signalled. We argue that it is essential to take a systemic perspective for enabling well-placed trust and trustworthy behavior in the long term. For our analysis we draw on relevant research from sociology, economics, and psychology, as well as HCI. We identify contextual properties (motivation based on temporal, social, and institutional embeddedness) and the actor's intrinsic properties (ability, and motivation based on internalized norms and benevolence) that form the basis of trustworthy behavior. Our analysis provides a frame of reference for the design of studies on trust in technology-mediated interactions, as well as a guide for identifying trust requirements in design processes. We demonstrate the application of the framework in three scenarios: call centre interactions, B2C e-commerce, and voice-enabled on-line gaming

An Eye Gaze Model for Controlling the Display of Social Status in Believable Virtual Humans

Abstract—Designing highly believable characters remains a major concern within digital games. Matching a chosen personality and other dramatic qualities to displayed behavior is an important part of improving overall believability. Gaze is a critical component of social exchanges and serves to make characters engaging or aloof, as well as to establish character’s role in a conversation. In this paper, we investigate the communication of status related social signals by means of a virtual human’s eye gaze. We constructed a cross-domain verbal-conceptual computational model of gaze for virtual humans to facilitate the display of social status. We describe the validation of the model’s parameters, including the length of eye contact and gazes, movement velocity, equilibrium response, and head and body posture. In a first set of studies, conducted on Amazon Mechanical Turk using prerecorded video clips of animated characters, we found statistically significant differences in how the characters’ status was rated based on the variation in social status. In a second step based on these empirical findings, we designed an interactive system that incorporates dynamic eye tracking and spoken dialog, along with real-time control of a virtual character. We evaluated the model using a presential, interactive scenario of a simulated hiring interview. Corroborating our previous finding, the interactive study yielded significant differences in perception of status were found (p = .046). Thus, we believe status is an important aspect of dramatic believability, and accordingly, this paper presents our social eye gaze model for realistic procedurally animated characters and shows its efficacy. Index Terms—procedural animation, believable characters, virtual human, gaze, social interaction, nonverbal behaviour, video game