Are you a human or a humanoid: Predictive user modelling through behavioural analysis of online gameplay data

Intelligent agents are widely used in robotics, gaming and simulation. A key issue is modelling human behaviours so that intelligent agents can use a human’s behavioural model to imitate them and predict their next moves. In this article, we use Internet-based multiplayer online gaming (MOG) as a case study to present our approach to predictive user modelling through behavioural analysis of online gameplay data. As latency is an inherited bottleneck of the Internet and is likely to remain so into a foreseeable future, a lot of efforts have been made to address the resulting issues. Most of the existing latency handling techniques are based on the assumption that latency is within an acceptable threshold so that they can alleviate or even completely hide its negative impact on players’ quality of experience (QoE) that directly determines consumers’ satisfaction of the provided MOG services. While this assumption is mostly valid, it is worth noting that a player’s Internet connection latency always fluctuates (known as jitter), possibly to the extent of exceeding a MOG’s designated threshold in which case none of the techniques can handle properly but disconnecting the player from the gameplay session. Forcing a player to quit prematurely simply due to a spike of unusual high latency has a significant negative impact both on the gameplay’s fairness and on the player’s QoE. To improve customer satisfaction of a MOG service, we propose a more tolerant approach by temporarily substituting a player with a humanoid bot in the event of latency hikes so that the player always remains in the gameplay session. The challenge in this approach is to create a personalised humanoid bot that can imitate the playing pattern of the individual human player being substituted. Our solution is to first extract key variables that have impact on the human player’s decision-makings through behavioural analysis of the player’s historical gameplay data, then model the relationships among these variables, and finally creates the player’s humanoid bot with the model. In this paper, we use a multiplayer online pong game as a case study to explain behavioural variables, modelling techniques, processes, outcomes, and performance studies.     

Cane-supported walking by humanoid robot and falling-factor-based optimal cane usage selection

In this study, we propose a scheme that enables a humanoid robot to perform cane-supported walking and to select the optimal cane usage depending on its condition. A reaction force is generated through the cane–ground interaction. Although the optimal cane–ground contact position leads to the most effective reaction force from the viewpoint of the walking capability, the cane position is constrained during such contact. Thus, different cane utilities can be achieved based on the priority assigned to the reaction force and cane position. In this light, we propose cyclic, leg-like, and preventive usages, each of which has different priorities. These different priorities lead to different utilities such as the expansion of the support polygon, load distribution, and posture recovery. The robot selects the most suitable of these three cane usages using a selection algorithm for locomotion that is combined with Q-learning (Us-SAL). Through simulations, the robot is made to learn the affinities between these cane usages and falling factors as Q-values and to accordingly select the optimal cane usage. As a result, the robot can perform the desired walking by selecting the optimal cane usage depending on its condition. We conducted walking experiments for each cane usage and found that each improved the walking stability for a suitable falling factor. Furthermore, we experimentally validated Us-SAL; the robot selected the optimal cane usage depending on its condition and walked in a complex environment without falling.     

The DCM generalized inverse: efficient body-wrench distribution in multi-contact balance control

A computationally efficient solution of the body- wrench distribution problem for bipeds and multi-legged robots is introduced. The method is based on a weighted generalized inverse, the weights being determined from relationships pertinent to the divergent component of motion (DCM), base-of-support (BoS) geometry, friction constraints and center of pressure allocation. The user (or the robot) specifies appropriate weights only indirectly, by setting the desired contact transition boundaries within the net BoS. It is shown that the proposed weighted generalized inverse ensures body- wrench distribution in a way consistent with both the static and dynamic states. The dependency on the DCM yields an important advantage when the method is applied to reactive balance control in response to unknown disturbances. An admittance-type stabilizer is obtained by setting the reference DCM at the current center of mass position. This stabilizer does not require reference values for the centers of pressures and the contact wrenches. The method is implemented with a whole-body, torque-based balance controller. Its performance is examined through simulations with planar and non-coplanar contacts, during proactive and reactive tasks.     

仿人按摩机器人手臂结构设计及改进

仿人按摩机器人手臂是以传统中医按摩理论为基础,结合机器人定位精度高,按摩力量精确可控,动作可准确重复,不会产生疲劳等特点,构建了中医按摩机器人平台。机器人手臂是仿人按摩机器人的关键,在深入研究按摩机器人手臂的工作机理基础上,充分考虑手臂的材料、结构和刚度以及手臂反应的灵活性、稳定性、安全性、拟人化等要求,对大臂、肩关节、肘关节、小臂、手腕等关键环节进行了设计及改进。经过性能对比分析,设计的仿人按摩机器人手臂达到设计要求。     

仿人机器人行走间隙碰撞系统混沌动力学分析

为研究仿人机器人行走过程中由间隙碰撞引起的行走不稳定问题,建立了一类含间隙碰撞的三自由度动力学模型,与现有模型相比能够更好地模拟行走过程中脚与地面之间的间隙碰撞过程。结合该动力学模型推导出数学模型,进而运用模态叠加法计算了该碰撞系统在周期激励下的动力学响应。把庞加莱截面作为间隙碰撞界面,对该碰撞系统进行混沌动力学分析,发现其具有混沌运动特征。研究结果为仿人机器人行走间隙碰撞系统的研究和控制提供了理论依据。     

The boundary of racial prejudice: Comparing preferences for computer-synthesized White,Black, and robot characters

Humanoid social robots are predicted to interact with humans in various domains of social life as robot technology keeps advancing. One area for understanding the impact of robots on human society is interracial relations. Would robots constitute a nonhuman outgroup to trigger human ingroup favoritism which will confine the boundary of racial prejudice? A study (N = 105) assessed Whites’ rank-ordered preferences for 15 White, Black and robot computer-synthesized characters. Explicit racial prejudice positively predicted White versus Black character preferences for liking and as one’s avatar, virtual friend, and virtual tutor. The implicit racial prejudice, measured with the Implicit Association Test (IAT), provided additional predictive utility for virtual friend. Among the 64 participants who reported minimal interest in robots, explicit racial prejudice negatively predicted preferences for Black over robot characters, showing a pattern that individuals with high prejudice preferred robot characters over Black ones. The results suggest alarming strength of racial prejudice and cast doubt on the notion of all-human ingroup favoritism in comparison to robots.     

Integration of humanoid robots in collaborative working environment: a case study on motion generation

This paper illustrates through a practical example an integration of a humanoid robotic architecture, with an open-platform collaborative working environment called BSCW (Be Smart-Cooperate Worldwide). BSCW is primarily designed to advocate a futuristic shared workspace system for humans. We exemplify how a complex robotic system (such as a humanoid robot) can be integrated as a proactive collaborative agent which provides services and interacts with other agents sharing the same collaborative environment workspace. Indeed, the robot is seen as a ‘user’ of the BSCW which is able to handle simple tasks and reports on their achievement status. We emphasis on the importance of using standard software such as CORBA (Common Object Request Broker Architecture) in order to easily build interfaces between several interacting complex software layers, namely from real-time constraints up to basic Internet data exchange.     

A dynamic simulator for humanoid robots

Computer simulation is an essential step in the design and construction of various mechanical structures, including biped robots, because it enables rapid testing and virtual prototyping during the construction phase. Although many different simulators are available, this article gives an overview and a motivation for building a new dynamic multibody simulator. The simulator is especially adapted to humanoid robot Archie, developed at the IHRT Institute at the Technical University of Vienna. In addition, it is shown how the simulator can be used not only in the controller design, but also in the online control loop to extend the available sensors: a virtual sensors principle. This work was presented in part at the First European Workshop on Artificial Life and Robtics, Vienna, Austria, July 12–13, 2007     

A selective attention-based contextual perception approach for a humanoid robot

A humanoid robot is always flooded by sensed information when sensing the environment, and it usually needs significant time to compute and process the sensed information. In this paper, a selective attention-based contextual perception approach was proposed for humanoid robots to sense the environment with high efficiency. First, the connotation of attention window (AW) is extended to make a more general and abstract definition of AW, and its four kinds of operations and state transformations are also discussed. Second, the attention control policies are described, which integrate intensionguided perceptual objects selection and distractor inhibition, and can deal with emergent issues. Distractor inhibition is used to filter unrelated information. Last, attention policies are viewed as the robot’s perceptual modes, which can control and adjust the perception efficiency. The experimental results show that the presented approach can promote the perceptual efficiency significantly, and the perceptual cost can be effectively controlled through adopting different attention policies.     

A selective attention-based contextual perception approach for a humanoid robot

A humanoid robot is always flooded by sensed information when sensing the environment, and it usually needs significant time to compute and process the sensed information. In this paper, a selective attention-based contextual perception approach was proposed for humanoid robots to sense the environment with high efficiency. First, the connotation of attention window (AW) is extended to make a more general and abstract definition of AW, and its four kinds of operations and state transformations are also discussed. Second, the attention control policies are described, which integrate intensionguided perceptual objects selection and distractor inhibition, and can deal with emergent issues. Distractor inhibition is used to filter unrelated information. Last, attention policies are viewed as the robot’s perceptual modes, which can control and adjust the perception efficiency. The experimental results show that the presented approach can promote the perceptual efficiency significantly, and the perceptual cost can be effectively controlled through adopting different attention policies.