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Introduction As mobile robots proliferate in communities, designers must consider the impacts these systems have on the users, onlookers, and places they encounter. It becomes increasingly necessary to study situations where humans and robots coexist in common spaces, even if they are not directly interacting. This dataset presents a multidisciplinary approach to study human-robot encounters in an indoor apartment-like setting between participants and two mobile robots. Participants take questionnaires, wear sensors for physiological measures, and take part in a focus group after experiments finish. This dataset contains raw time series data from sensors and robots, and qualitative results from focus groups. The data can be used to analyze measures of human physiological response to varied encounter conditions, and to gain insights into human preferences and comfort during community encounters with mobile robots. Dataset Contents A dictionary of terms found in the dataset can be found in the "Data-Dictionary.pdf" Synchronized XDF files from every trial with raw data from electrodermal activity (EDA), electrocardiography (ECG), photoplethysmography (PPG) and seismocardiography (SCG). These synchronized files also contain robot pose data and microphone data. Results from analysis of two important features found from heart rate variability (HRV) and EDA. Specifically, HRV_CMSEn and nsEDRfreq is computed for each participant over each trial. These results also include Robot Confidence, which is a classification score representing the confidence that the 80 physiological features considered originate from a subject in a robot encounter. The higher the score, the higher the confidence A vectormap of the environment used during testing ("AHG_vectormap.txt") and a csv with locations of participant seating within the map ("Participant-Seating-Coordinates.csv"). Each line of the vectormap represents two endpoints of a line: x1,y1,x2,y2. The coordinates of participant seating are x,y positions and rotation about the vertical axis in radians. Anonymized videos captured using two static cameras placed in the environment. They are located in the living room and small room, respectively. Animations visualized from XDF files that show participant location, robot behaviors and additional characteristics like participant-robot line-of-sight and relative audio volume. Quotes associated with themes taken from focus group data. These quotes demonstrate and justify the results of the thematic analysis. Raw text from focus groups is not included for privacy concerns. Quantitative results from focus groups associated with factors influencing perceived safety. These results demonstrate the findings from deductive content analysis. The deductive codebook is also included. Results from pre-experiment and between-trial questionnaires Copies of both questionnaires and the semi-structured focus group protocol. Human Subjects This dataset contain de-identified information for 24 total subjects over 13 experiment sessions. The population for the study is the students, faculty and staff at the University of Texas at Austin. Of the 24 participants, 18 are students and 6 are staff at the university. Ages range from 19-48 and there are 10 males and 14 females who participated. Published data has been de-identified in coordination with the university Internal Review Board. All participants signed informed consent to participate in the study and for the distribution of this data. Access Restrictions Transcripts from focus groups are not published due to privacy concerns. Videos including participants are de-identified with overlays on videos. All other data is labeled only by participant ID, which is not associated with any identifying characteristics. Experiment Design Robots This study considers indoor encounters with two quadruped mobile robots. Namely, the Boston Dynamics Spot and Unitree Go1. These mobile robots are capable of everyday movement tasks like inspection, search or mapping which may be common tasks for autonomous agents in university communities. The study focus on perceived safety of bystanders under encounters with these relevant platforms. Control Conditions and Experiment Session Layout We control three variables in this study: Participant seating social (together in the living room) v. isolated (one in living room, other in small room) Robots Together v. Separate Robot Navigation v. Search Behavior A visual representation of the three control variables are shown on the left in (a)-(d) including the robot behaviors and participant seating locations, shown as X's. Blue represent social seating and yellow represent isolated seating. (a) shows the single robot navigation path. (b) is the two robot navigation paths. In (c) is the single robot search path and (d) shows the two robot search paths. The order of behaviors and seating locations are randomized and then inserted into the experiment session as overviewed in (e). These experiments are designed to gain insights into human responses to encounters with robots. The first step is receiving consent from the followed by a pre-experiment questionnaire that documents demographics, baseline stress information and big 5 personality traits. The nature video is repeated before and after the experimental session to establish a relaxed baseline physiological state. Experiments take place over 8 individual trials, which are defined by a subject seat arrangement, search or navigation behavior, and robots together or separate. After each of the 8 trials, participants take the between trial questionnaire, which is a 7 point Likert scale questionnaire designed to assess perceived safety during the preceding trial. After experiments and sensor removal, participants take part in a focus group. Synchronized Data Acquisition Data is synchronized from physiological sensors, environment microphones and the robots using the architecture shown. These raw xdf files are named using the following file naming convention: Trials where participants sit together in the living room [Session number]-[trial number]-social-[robots together or separate]-[search or navigation behavior].xdf Trials where participants are isolated [Session number]-[trial number]-isolated-[subject ID living room]-[subject ID small room]-[robots together or separate]-[search or navigation behavior].xdf Qualitative Data Qualitative data is obtained from focus groups with participants after experiments. Typically, two participants take part however two sessions only included one participant. The semi-structured focus group protocol can be found in the dataset. Two different research methods are applied to focus group transcripts. Note: the full transcripts are not provided for privacy concerns. First, we performed a qualitative content analysis using deductive codes found from an existing model of perceived safety during HRI (Akalin et al. 2023). The quantitative results from this analysis are reported as frequencies of references to the various factors of perceived safety. The codebook describing these factors is included in the dataset. Second, an inductive thematic analysis was performed on the data to identify emergent themes. The resulting themes and associated quotes taken from focus groups are also included. Data Organization Data is organized in separate folders, namely: animation-videos anonymized-session-videos focus-group-results questionnaire-responses research-materials signal-analysis-results synchronized-xdf-data Data Quality Statement In limited trials, participant EDA or ECG signals or robot pose information may be missing due to connectivity issues during data acquisition. Additionally, the questionnaires for Participant ID0 and ID1 are incomplete due to an error in the implementation of the Qualtrics survey instrument used.more » « less
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This paper proposes an online gain adaptation approach to enhance the robustness of whole-body control (WBC) framework for legged robots under unknown external force disturbances. Without properly accounting for external forces, the closed-loop control system incorporating WBC may become unstable, and therefore the desired task goals may not be achievable. To study the effects of external disturbances, we analyze the behavior of our current WBC framework via the use of both full-body and centroidal dynamics. In turn, we propose a way to adapt feedback gains for stabilizing the controlled system automatically. Based on model approximations and stability theory, we propose three conditions to ensure that the adjusted gains are suitable for stabilizing a robot under WBC. The proposed approach has four contributions. We make it possible to estimate the unknown disturbances without force/torque sensors. We then compute adaptive gains based on theoretic stability analysis incorporating the unknown forces at the joint actuation level. We demonstrate that the proposed method reduces task tracking errors under the effect of external forces on the robot. In addition, the proposed method is easy-to-use without further modifications of the controllers and task specifications. The resulting gain adaptation process is able to run in real-time. Finally, we verify the effectiveness of our method both in simulations and experiments using the bipedal robot Draco2 and the humanoid robot Valkyrie .more » « less
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We propose a locomotion framework for bipedal robots consisting of a new motion planning method, dubbed trajectory optimization for walking robots plus (TOWR+), and a new whole-body control method, dubbed implicit hierarchical whole-body controller (IHWBC). For versatility, we consider the use of a composite rigid body (CRB) model to optimize the robot’s walking behavior. The proposed CRB model considers the floating base dynamics while accounting for the effects of the heavy distal mass of humanoids using a pre-trained centroidal inertia network. TOWR+ leverages the phase-based parameterization of its precursor, TOWR, and optimizes for base and end-effectors motions, feet contact wrenches, as well as contact timing and locations without the need to solve a complementary problem or integer program. The use of IHWBC enforces unilateral contact constraints (i.e., non-slip and non-penetration constraints) and a task hierarchy through the cost function, relaxing contact constraints and providing an implicit hierarchy between tasks. This controller provides additional flexibility and smooth task and contact transitions as applied to our 10 degree-of-freedom, line-feet biped robot DRACO. In addition, we introduce a new open-source and light-weight software architecture, dubbed planning and control (PnC), that implements and combines TOWR+ and IHWBC. PnC provides modularity, versatility, and scalability so that the provided modules can be interchanged with other motion planners and whole-body controllers and tested in an end-to-end manner. In the experimental section, we first analyze the performance of TOWR+ using various bipeds. We then demonstrate balancing behaviors on the DRACO hardware using the proposed IHWBC method. Finally, we integrate TOWR+ and IHWBC and demonstrate step-and-stop behaviors on the DRACO hardware.more » « less
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null (Ed.)Shared autonomy provides a framework where a human and an automated system, such as a robot, jointly control the system’s behavior, enabling an effective solution for various applications, including human-robot interaction and remote operation of a semi-autonomous system. However, a challenging problem in shared autonomy is safety because the human input may be unknown and unpredictable, which affects the robot’s safety constraints. If the human input is a force applied through physical contact with the robot, it also alters the robot’s behavior to maintain safety. We address the safety issue of shared autonomy in real-time applications by proposing a two-layer control framework. In the first layer, we use the history of human input measurements to infer what the human wants the robot to do and define the robot’s safety constraints according to that inference. In the second layer, we formulate a rapidly-exploring random tree of barrier pairs, with each barrier pair composed of a barrier function and a controller. Using the controllers in these barrier pairs, the robot is able to maintain its safe operation under the intervention from the human input. This proposed control framework allows the robot to assist the human while preventing them from encountering safety issues. We demonstrate the proposed control framework on a simulation of a two-linkage manipulator robot.more » « less