Despite the rise of mobile robot deployments in community settings, the perceived safety of cohabitants remains understudied in many domains. To address this gap, we perform a study to identify elements of indoor human–mobile robot encounters that impact perceived safety. This study evaluates the effects of robot movement behavior and the number of robots nearby on perceived safety of participants. Further, this article investigates how the presence of other people impacts perceived safety in such settings. We leverage methodologies from physiological signal analysis, autonomy, surveys, and qualitative interviews to decode insights into the human experience during such encounters. Particularly, signal analysis yielded that the presence of multiple robots decreases perceived safety and that search behaviors were more comfortable than navigation behaviors. Similarly, interviews with participants demonstrated clear effects on perceived safety in the presence of others, and that sensemaking was a key component involved in their perceptions of safety. When the data were combined, interview data revealed that near collisions between the robots likely confounded the signal analysis findings with respect to the number of robots and their movement behavior. The data types agree that the presence of a robot impacts perceived safety; however, there are also conflicting results that we discuss, which highlight that near-collisions impact perceived safety. In aggregate, the study illustrates the benefits of leveraging eclectic methods to ascertain deeper insights. Overall, the article aims to unlock insights into human perceptions during encounters with community embedded robots, which can be used in the future design of such systems.

Despite the rise of mobile robot deployments in home and work settings, perceived safety of users and bystanders is understudied in the human-robot interaction (HRI) literature. To address this, we present a study designed to identify elements of a human-robot encounter that correlate with observed stress response. Stress is a key component of perceived safety and is strongly associated with human physiological response. In this study a Boston Dynamics Spot and a Unitree Go1 navigate autonomously through a shared environment occupied by human participants wearing multimodal physiological sensors to track their electrocardiography (ECG) and electrodermal activity (EDA). The encounters are varied through several trials and participants self-rate their stress levels after each encounter. The study resulted in a multidimensional dataset archiving various objective and subjective aspects of a human-robot encounter, containing insights for understanding perceived safety in such encounters. To this end, acute stress responses were decoded from the human participants’ ECG and EDA and compared across different human-robot encounter conditions. Statistical analysis of data indicate that on average (1) participants feel more stress during encounters compared to baselines, (2) participants feel more stress encountering multiple robots compared to a single robot and (3) participants stress increases during navigation behavior compared with search behavior.

We tackle the problem of perceptive locomotion in dynamic environments. In this problem, a quadrupedal robot must exhibit robust and agile walking behaviors in response to environmental clutter and moving obstacles. We present a hierarchical learning framework, named PRELUDE, which decomposes the problem of perceptive locomotion into high-level decision-making to predict navigation commands and low-level gait generation to realize the target commands. In this framework, we train the high-level navigation controller with imitation learning on human demonstrations collected on a steerable cart and the low-level gait controller with reinforcement learning (RL). Therefore, our method can acquire complex navigation behaviors from human supervision and discover versatile gaits from trial and error. We demonstrate the effectiveness of our approach in simulation and with hardware experiments. Videos and code can be found at the project page: https://ut-austin-rpl.github.io/PRELUDE.

We present a method that finds locomanipulation plans that perform simultaneous locomotion and manipulation of objects for a desired end-effector trajectory. Key to our approach is to consider an injective locomotion constraint manifold that defines the locomotion scheme of the robot and then using this constraint manifold to search for admissible manipulation trajectories. The problem is formulated as a weighted-A* graph search whose planner output is a sequence of contact transitions and a path progression trajectory to construct the whole-body kinodynamic locomanipulation plan. We also provide a method for computing, visualizing, and learning the locomanipulability region, which is used to efficiently evaluate the edge transition feasibility during the graph search. Numerical simulations are performed with the NASA Valkyrie robot platform that utilizes a dynamic locomotion approach, called the divergent-component-of-motion (DCM), on two example locomanipulation scenarios.