Tracking Anthropomorphizing Behavior in Human-Robot Interaction

Existing methodologies to describe anthropomorphism in human-robot interaction often rely either on specific one-time responses to robot behavior, such as keeping the robot's secret, or on post hoc measures, such as questionnaires. Currently, there is no method to describe the dynamics of people's behavior over the course of an interaction and in response to robot behavior. In this paper, I propose a method that allows the researcher to trace anthropomorphizing and non-anthropomorphizing responses to robots dynamically moment-by-moment over the course of human-robot interactions. I illustrate this methodology in a case study and find considerable variation between participants, but also considerable intrapersonal variation in the ways the robot is anthropomorphized. That is, people may respond to the robot as if it was another human in one moment and to its machine-like properties in the next. These findings may influence explanatory models of anthropomorphism.

Does the Correspondence Bias Apply to Social Robots?: Dispositional and Situational Attributions of Human Versus Robot Behavior

Increasingly, people interact with embodied machine communicators and are challenged to understand their natures and behaviors. The Fundamental Attribution Error (FAE, sometimes referred to as the correspondence bias) is the tendency for individuals to over-emphasize personality-based or dispositional explanations for other people’s behavior while under-emphasizing situational explanations. This effect has been thoroughly examined with humans, but do people make the same causal inferences when interpreting the actions of a robot? As compared to people, social robots are less autonomous and agentic because their behavior is wholly determined by humans in the loop, programming, and design choices. Nonetheless, people do assign robots agency, intentionality, personality, and blame. Results of an experiment showed that participants made correspondent inferences when evaluating both human and robot speakers, attributing their behavior to underlying attitudes even when it was clearly coerced. However, they committed a stronger correspondence bias in the case of the robot–an effect driven by the greater dispositional culpability assigned to robots committing unpopular behavior–and they were more confident in their attitudinal judgments of robots than humans. Results demonstrated some differences in the global impressions of humans and robots based on behavior valence and choice. Judges formed more generous impressions of the robot agent when its unpopular behavior was coerced versus chosen; a tendency not displayed when forming impressions of the human agent. Implications of attributing robot behavior to disposition, or conflating robot actors with their actions, are addressed.

The Need for Verbal Robot Explanations and How People Would Like a Robot to Explain Itself

Although non-verbal cues such as arm movement and eye gaze can convey robot intention, they alone may not provide enough information for a human to fully understand a robot’s behavior. To better understand how to convey robot intention, we conducted an experiment ( N = 366 ) investigating the need for robots to explain , and the content and properties of a desired explanation such as timing , engagement importance , similarity to human explanations, and summarization . Participants watched a video where the robot was commanded to hand an almost-reachable cup and one of six reactions intended to show the unreachability : doing nothing (No Cue), turning its head to the cup (Look), or turning its head to the cup with the addition of repeated arm movement pointed towards the cup (Look & Point), and each of these with or without a Headshake. The results indicated that participants agreed robot behavior should be explained across all conditions, in situ , in a similar manner as what human explain, and provide concise summaries and respond to only a few follow-up questions by participants. Additionally, we replicated the study again with N = 366 participants after a 15-month span and all major conclusions still held.

Are Preferences Useful for Better Assistance?

Assistive Robots have an inherent need of adapting to the user they are assisting. This is crucial for the correct development of the task, user safety, and comfort. However, adaptation can be performed in several manners. We believe user preferences are key to this adaptation. In this article, we evaluate the use of preferences for Physically Assistive Robotics tasks in a Human-Robot Interaction user evaluation. Three assistive tasks have been implemented consisting of assisted feeding, shoe-fitting, and jacket dressing, where the robot performs each task in a different manner based on user preferences. We assess the ability of the users to determine which execution of the task used their chosen preferences (if any). The obtained results show that most of the users were able to successfully guess the cases where their preferences were used even when they had not seen the task before. We also observe that their satisfaction with the task increases when the chosen preferences are employed. Finally, we also analyze the user’s opinions regarding assistive tasks and preferences, showing promising expectations as to the benefits of adapting the robot behavior to the user through preferences.

Monitoring Weeder Robots and Anticipating Their Functioning by Using Advanced Topological Data Analysis

The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Most of existing methodologies enabling efficient diagnosis are based on the data analysis, and in particular on some statistical quantities derived from the data. The present work explores the use of an original approach that instead of analyzing quantities derived from the data, analyzes the “shape” of the data, that is, the time series topology based on the homology persistence. We will prove that this procedure is able to extract valuable patterns able to discriminate the trajectories that the robot follows depending on the particular patch in which it operates, as well as to differentiate the robot behavior before and after undergoing a maintenance operation. Even if it is a preliminary work, and it does not pretend to compare its performances with respect to other existing technologies, this work opens new perspectives in considering quite natural and simple descriptors based on the intrinsic information that data contains, with the aim of performing efficient diagnosis and prognosis.

Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials

Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. However, BCI performance may vary due to the non-stationary nature of the electroencephalogram (EEG) signals. It, hence, cannot be used safely for controlling tasks where errors may be detrimental to the user. Avoiding obstacles is one such task. As there exist many techniques to avoid obstacles in robotics, we propose to give the control to the robot to avoid obstacles and to leave to the user the choice of the robot behavior to do so a matter of personal preference as some users may be more daring while others more careful. We enable the users to train the robot controller to adapt its way to approach obstacles relying on BCI that detects error-related potentials (ErrP), indicative of the user’s error expectation of the robot’s current strategy to meet their preferences. Gaussian process-based inverse reinforcement learning, in combination with the ErrP-BCI, infers the user’s preference and updates the obstacle avoidance controller so as to generate personalized robot trajectories. We validate the approach in experiments with thirteen able-bodied subjects using a robotic arm that picks up, places and avoids real-life objects. Results show that the algorithm can learn user’s preference and adapt the robot behavior rapidly using less than five demonstrations not necessarily optimal.

Exploring Behavioral Creativity of a Proactive Robot

Creativity, in one sense, can be seen as an effort or action to bring novelty. Following this, we explore how a robot can be creative by bringing novelty in a human–robot interaction (HRI) scenario. Studies suggest that proactivity is closely linked with creativity. Proactivity can be defined as acting or interacting by anticipating future needs or actions. This study aims to explore the effect of proactive behavior and the relation of such behaviors to the two aspects of creativity: 1) the perceived creativity observed by the user in the robot’s proactive behavior and 2) creativity of the user by assessing how creativity in HRI can be shaped or influenced by proactivity. We do so by conducting an experimental study, where the robot tries to support the user on the completion of the task regardless of the end result being novel or not and does so by exhibiting anticipatory proactive behaviors. In our study, the robot instantiates a set of verbal communications as proactive robot behavior. To our knowledge, the study is among the first to establish and investigate the relationship between creativity and proactivity in the HRI context, based on user studies. The initial results have indicated a relationship between observed proactivity, creativity, and task achievement. It also provides valuable pointers for further investigation in this domain.

A Nonlinear UAV Control Tuning Under Communication Delay using HPC Strategies in Parameters Space

In practical applications, the presence of delays can deteriorate the performance of the control system or even cause plant instability. However, by properly controlling these delays, it is possible to improve the performance of the mechanism. The present work is based on a proposal to analyze the asymptotic stability and convergence of a quadrotor robot, an unmanned aerial vehicle (UAV), on the performance of a given task, under time delay in the data flow. The effects of the communication delay problem, as well as the response-signal behavior of the quadrotors in the accomplishment of positioning mission are presented and analyzed from the insertion of fixed time delay intervals in the UAVs' data collected by its sensors system. Due to the large search space in the set of parameter combinations and the high computational cost required to perform such an analysis by sequentially executing thousands of simulations, this work proposes an open source GPU-based implementation to simulate the robot behavior. Experimental results show a speedup up to 4900x in comparison to MATLAB® implementation. The implement is available in Colab Google platform.