Eye contact between strangers, even fleeting, can spark interaction and foster connection, happiness, and belonging. Yet in public spaces, such encounters are often suppressed by “civil inattention,” with many people absorbed in their phones. We explore how reconfiguring the ambient environment with MirrorBot, a mobile robot with adaptive mirrors, can encourage social encounters by subtly redirecting glances. By shifting reflections between self- and mutual recognition, MirrorBot invites serendipitous eye contact, shared awareness, and low-stakes engagement. In a controlled 2×2 between-subjects study with 90 participants (45 dyads) across four conditions (MirrorBot, Bot-only, Mirror-only, and Control), we found that MirrorBot led participants to initiate conversation more often, feel greater closeness and togetherness, and have more enjoyable interactions. Our findings position robots not only as social agents but as socio-spatial interfaces that choreograph sight lines and shared attention in physical space, opening new possibilities for technologies that cultivate human connection in public life.

Millions of people around the world need assistance with feeding. Robotic feeding systems offer the potential to enhance autonomy and quality of life for individuals with impairments and reduce caregiver workload. However, their widespread adoption has been limited by technical challenges such as estimating bite timing, the appropriate moment for the robot to transfer food to a user’s mouth. In this work, we introduce WAFFLE: Wearable Approach For Feeding with LEarned Bite Timing, a system that accurately predicts bite timing by leveraging wearable sensor data to be highly reactive to natural user cues such as head movements, chewing, and talking. We train a supervised regression model on bite timing data from 14 participants and incorporate a user-adjustable assertiveness threshold to convert predictions into proceed or stop commands. In a study with 15 participants without motor impairments with the Obi feeding robot, WAFFLE performs statistically on par with or better than baseline methods across measures of feeling of control, robot understanding, and workload, and is preferred by the majority of participants for both individual and social dining. We further demonstrate WAFFLE’s generalizability in a study with 2 participants with motor impairments in their home environments using a Kinova 7DOF robot. Our findings support WAFFLE’s effectiveness in enabling natural, reactive bite timing that generalizes across users, robot hardware, robot positioning, feeding trajectories, foods, and both individual and social dining contexts. Videos are located at https://sites.google.com/view/bitetiming/.

Bed-to-wheelchair transferring is a ubiquitous activity of daily living (ADL), but especially challenging for caregiving robots with limited payloads. We develop a novel algorithm that leverages the presence of other assistive devices: a Hoyer sling and a wheelchair for coarse manipulation of heavy loads, alongside a robot arm for fine-grained manipulation of deformable objects (Hoyer sling straps). We instrument the Hoyer sling and wheelchair with actuators and sensors so that they can become intelligent agents in the algorithm. We then focus on one subtask of the transferring ADL-tying Hoyer sling straps to the sling bar-that exemplifies the challenges of transfer: multi-agent planning, deformable object manipulation, and generalization to varying hook shapes, sling materials, and care recipient bodies. To address these challenges, we propose CART-MPC, a novel algorithm based on turn-taking multi-agent model predictive control that uses a learned neural dynamics model for a keypoint-based representation of the deformable Hoyer sling strap, and a novel cost function that leverages linking numbers from knot theory and neural amortization to accelerate inference. We validate it in both RCareWorld [1] simulation and real-world environments. In simulation, CART-MPC successfully generalizes across diverse hook designs, sling materials, and care recipient body shapes. In the real world, we show zero-shot sim-to-real generalization capabilities to tie deformable Hoyer sling straps on a sling bar towards transferring a manikin from a hospital bed to a wheelchair. See our websitefor supplementary materials: https://emprise.cs.cornell.edu/cart-mpc/.