People have a variety of preferences for how robots behave. To understand and reason about these preferences, robots aim to learn a reward function that describes how aligned robot behaviors are with a user's preferences. Good representations of a robot's behavior can significantly reduce the time and effort required for a user to teach the robot their preferences. Specifying these representations-what “features“ of the robot's behavior matter to users-remains a difficult problem; Features learned from raw data lack semantic meaning and features learned from user data require users to engage in tedious labeling processes. Our key insight is that users tasked with customizing a robot are intrinsically motivated to produce labels through exploratory search; they explore behaviors that they find interesting and ignore behaviors that are irrelevant. To harness this novel data source of exploratory actions, we propose contrastive learning from exploratory actions (CLEA) to learn trajectory features that are aligned with features that users care about. We learned CLEA features from exploratory actions users performed in an open-ended signal design activity$(N=25)$with a Kuri robot, and evaluated CLEA features through a second user study with a different set of users$(N=42)$. CLEA features outperformed self-supervised features when eliciting user preferences over four metrics: completeness, simplicity, minimality, and explainability.

Hair care robots can help address labor shortages in elderly care while enabling those with limited mobility to maintain their hair-related identity. We present MOE-Hair, a soft robot system that performs three hair-care tasks: head patting, finger combing, and hair grasping. The system features a tendon-driven soft robot end-effector (MOE) with a wrist-mounted RGBD camera, leveraging both mechanical compliance for safety and visual force sensing through deformation. In testing with a force-sensorized mannequin head, MOE achieved comparable hair-grasping effectiveness while applying significantly less force than rigid grippers. Our novel force estimation method combines visual deformation data and tendon tensions from actuators to infer applied forces, reducing sensing errors by up to 60.1% and 20.3% compared to actuator current load-only and depth image-only baselines, respectively. A user study with 12 participants demonstrated statistically significant preferences for MOE-Hair over a baseline system in terms of comfort, effectiveness, and appropriate force application. These results demonstrate the unique advantages of soft robots in contact-rich hair-care tasks, while highlighting the importance of precise force control despite the inherent compliance of the system. Videos, data, and code are available at moehair.github.io.

Bias evaluation benchmarks and dataset and model documentation have emerged as central processes for assessing the biases and harms of artificial intelligence (AI) systems. However, these auditing processes have been criticized for their failure to integrate the knowledge of marginalized communities and consider the power dynamics between auditors and the communities. Consequently, modes of bias evaluation have been proposed that engage impacted communities in identifying and assessing the harms of AI systems (e.g., bias bounties). Even so, asking what marginalized communities want from such auditing processes has been neglected. In this paper, we ask queer communities for their positions on, and desires from, auditing processes. To this end, we organized a participatory workshop to critique and redesign bias bounties from queer perspectives. We found that when given space, the scope of feedback from workshop participants goes far beyond what bias bounties afford, with participants questioning the ownership, incentives, and efficacy of bounties. We conclude by advocating for community ownership of bounties and complementing bounties with participatory processes (e.g., co-creation).

Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutions to a given problem. To grow further, we believe the QD community faces two challenges: developing a framework to represent the field's growing array of algorithms, and implementing that framework in software that supports a range of researchers and practitioners. To address these challenges, we have developed pyribs, a library built on a highly modular conceptual QD framework. By replacing components in the conceptual framework, and hence in pyribs, users can compose algorithms from across the QD literature; equally important, they can identify unexplored algorithm variations. Furthermore, pyribs makes this framework simple, flexible, and accessible, with a user-friendly API supported by extensive documentation and tutorials. This paper overviews the creation of pyribs, focusing on the conceptual framework that it implements and the design principles that have guided the library's development. Pyribs is available at https://pyribs.org

Mindfulness-based therapies have been shown to be effective in improving mental health, and technology-based methods have the potential to expand the accessibility of these therapies. To enable real-time personalized content generation for mindfulness practice in these methods, high-quality computer-synthesized text-to-speech (TTS) voices are needed to provide verbal guidance and respond to user performance and preferences. However, the user-perceived quality of state-of-the-art TTS voices has not yet been evaluated for administering mindfulness meditation, which requires emotional expressiveness. In addition, work has not yet been done to study the effect of physical embodiment and personalization on the user-perceived quality of TTS voices for mindfulness. To that end, we designed a two-phase human subject study. In Phase 1, an online Mechanical Turk between-subject study (N=471) evaluated 3 (feminine, masculine, child-like) state-of-the-art TTS voices with 2 (feminine, masculine) human therapists' voices in 3 different physical embodiment settings (no agent, conversational agent, socially assistive robot) with remote participants. Building on findings from Phase 1, in Phase 2, an in-person within-subject study (N=94), we used a novel framework we developed for personalizing TTS voices based on user preferences, and evaluated user-perceived quality compared to best-rated non-personalized voices from Phase 1. We found that the best-rated human voice was perceived better than all TTS voices; the emotional expressiveness and naturalness of TTS voices were poorly rated, while users were satisfied with the clarity of TTS voices. Surprisingly, by allowing users to fine-tune TTS voice features, the user-personalized TTS voices could perform almost as well as human voices, suggesting user personalization could be a simple and very effective tool to improve user-perceived quality of TTS voice.

When deploying robots, its physical characteristics, role, and tasks are often fixed. Such factors can also be associated with gender stereotypes among humans, which then transfer to the robots. One factor that can induce gendering but is comparatively easy to change is the robot’s voice. Designing voice in a way that interferes with fixed factors might therefore be a way to reduce gender stereotypes in human-robot interaction contexts. To this end, we have conducted a video-based online study to investigate how factors that might inspire gendering of a robot interact. In particular, we investigated how giving the robot a gender-ambiguous voice can affect perception of the robot. We compared assessments (n=111) of videos in which a robot’s body presentation and occupation mis/matched with human gender stereotypes. We found evidence that a gender-ambiguous voice can reduce gendering of a robot endowed with stereotypically feminine or masculine attributes. The results can inform more just robot design while opening new questions regarding the phenomenon of robot gendering.

The physical design of a robot suggests expectations of that robot’s functionality for human users and collaborators. When those expectations align with the robot’s true capabilities, users are more likely to adopt the technologies for their intended use. However, the relationship between expectations and socially interactive robot design is not well understood. This article applies the concept of design metaphors to robot design and contributes the Metaphors for Understanding Functional and Social Anticipated Affordances dataset of 165 extant robots and the expectations users place on them. We used Mechanical Turk to crowd-source user expectation over three user studies. The first study ( N = 382) associated crowd-sourced design metaphors to different robot embodiments. The second study ( N = 803) assessed initial social expectations of robot embodiments. The final study ( N = 805) addressed the degree of abstraction of the design metaphors and the functional expectations projected on robot embodiments. We performed analyses to gain insights into how design metaphors can be used to understand social and functional expectations of robots and how these data can be visualized to be useful for study designers and robot designers. Together, these results can serve to guide robot designers toward aligning user expectations with true robot capabilities, facilitating positive human–robot interaction.

This work introduces an approach for automatic hair combing by a lightweight robot. For people living with limited mobility, dexterity, or chronic fatigue, combing hair is often a difficult task that negatively impacts personal routines. We propose a modular system for enabling general robot manipulators to assist with a hair-combing task. The system consists of three main components. The first component is the segmentation module, which segments the location of hair in space. The second component is the path planning module that proposes automatically-generated paths through hair based on user input. The final component creates a trajectory for the robot to execute. We quantitatively evaluate the effectiveness of the paths planned by the system with 48 users and qualitatively evaluate the system with 30 users watching videos of the robot performing a hair-combing task in the physical world. The system is shown to effectively comb different hairstyles.

Children and adults with cerebral palsy (CP) can have involuntary upper limb movements as a consequence of the symptoms that characterize their motor disability, leading to difficulties in communicating with caretakers and peers. We describe how a socially assistive robot may help individuals with CP to practice non-verbal communicative gestures using an active orthosis in a one-on-one number-guessing game. We performed a user study and data collection with participants with CP; we found that participants preferred an embodied robot over a screen-based agent, and we used the participant data to train personalized models of participant engagement dynamics that can be used to select personalized robot actions. Our work highlights the benefit of personalized models in the engagement of users with CP with a socially assistive robot and offers design insights for future work in this area.