How Does Delegation in Social Interaction Evolve Over Time? Navigation with a Robot for Blind People
- URL: http://arxiv.org/abs/2601.19851v1
- Date: Tue, 27 Jan 2026 18:00:00 GMT
- Title: How Does Delegation in Social Interaction Evolve Over Time? Navigation with a Robot for Blind People
- Abstract summary: We present a repeated exposure study with six blind participants who interacted with a navigation-assistive robot in a real-world museum.<n>Findings show that participants refined their strategies over time, developing clearer preferences about when to rely on the robot versus act independently.<n>This work provides insights into how strategies and preferences evolve with repeated interaction and offers design implications for robots that adapt to user needs over time.
- Score: 9.288035205251315
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Autonomy and independent navigation are vital to daily life but remain challenging for individuals with blindness. Robotic systems can enhance mobility and confidence by providing intelligent navigation assistance. However, fully autonomous systems may reduce users' sense of control, even when they wish to remain actively involved. Although collaboration between user and robot has been recognized as important, little is known about how perceptions of this relationship change with repeated use. We present a repeated exposure study with six blind participants who interacted with a navigation-assistive robot in a real-world museum. Participants completed tasks such as navigating crowds, approaching lines, and encountering obstacles. Findings show that participants refined their strategies over time, developing clearer preferences about when to rely on the robot versus act independently. This work provides insights into how strategies and preferences evolve with repeated interaction and offers design implications for robots that adapt to user needs over time.
Related papers
- Legible Shared Autonomy: Implicit Communication of Robot Belief through Motion [10.259015383410395]
Traditional shared autonomy systems provide assistance along efficient shortest paths toward inferred goals.<n>When the robot correctly infers the goal, users continue controlling because they cannot perceive understanding from ambiguous assistive motion.<n>We address this by introducing legible motion into shared autonomy, where robot actions must both advance toward the goal and clearly reveal which goal has been inferred.
arXiv Detail & Related papers (2026-06-29T06:35:21Z) - ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration [51.69384671495837]
ActiveGlasses is a system for learning robot manipulation from ego-centric human demonstrations with active vision.<n>A stereo camera mounted on smart glasses serves as the sole perception device for both data collection and policy inference.<n>To enable zero-transfer, we extract object trajectories from demonstrations and use an object-centric point-cloud policy to jointly predict manipulation and head movement.
arXiv Detail & Related papers (2026-04-09T17:59:08Z) - AURA: Multimodal Shared Autonomy for Real-World Urban Navigation [49.419374037785815]
Long-horizon navigation in complex urban environments relies heavily on continuous human operation.<n>We present Assistive Urban Robot Autonomy (AURA), a new multi-modal framework that decomposes urban navigation into high-level human instruction and low-level AI control.<n>AURA effectively follows human instructions, reduces manual operation effort, and improves navigation stability, while enabling online adaptation.
arXiv Detail & Related papers (2026-04-02T05:59:31Z) - Navigation beyond Wayfinding: Robots Collaborating with Visually Impaired Users for Environmental Interactions [23.06831135567015]
We present a collaborative human-robot approach that combines our robotic guide dog's precise sensing and localization capabilities with the user's ability to perform physical manipulation.<n> Evaluation results show that our system enables navigation that is safer, smoother, and more efficient than both a traditional white cane and a non-adaptive guiding system.
arXiv Detail & Related papers (2026-03-15T04:21:46Z) - FollowMe: a Robust Person Following Framework Based on Re-Identification
and Gestures [12.850149165791551]
Human-robot interaction (HRI) has become a crucial enabler in houses and industries for facilitating operational flexibility.
We developed a unified perception and navigation framework, which enables the robot to identify and follow a target person.
The Re-ID module can autonomously learn the features of a target person and use the acquired knowledge to visually re-identify the target.
arXiv Detail & Related papers (2023-11-21T20:59:27Z) - A Study on Learning Social Robot Navigation with Multimodal Perception [6.052803245103173]
We present a study on learning social robot navigation with multimodal perception using a large-scale real-world dataset.
We compare unimodal and multimodal learning approaches against a set of classical navigation approaches in different social scenarios.
The results show that multimodal learning has a clear advantage over unimodal learning in both dataset and human studies.
arXiv Detail & Related papers (2023-09-22T01:47:47Z) - Principles and Guidelines for Evaluating Social Robot Navigation
Algorithms [44.51586279645062]
Social robot navigation is difficult to evaluate because it involves dynamic human agents and their perceptions of the appropriateness of robot behavior.
Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a social navigation metrics framework to make it easier to compare results from different simulators, robots and datasets.
arXiv Detail & Related papers (2023-06-29T07:31:43Z) - SACSoN: Scalable Autonomous Control for Social Navigation [62.59274275261392]
We develop methods for training policies for socially unobtrusive navigation.
By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space.
We collect a large dataset where an indoor mobile robot interacts with human bystanders.
arXiv Detail & Related papers (2023-06-02T19:07:52Z) - Gesture2Path: Imitation Learning for Gesture-aware Navigation [54.570943577423094]
We present Gesture2Path, a novel social navigation approach that combines image-based imitation learning with model-predictive control.
We deploy our method on real robots and showcase the effectiveness of our approach for the four gestures-navigation scenarios.
arXiv Detail & Related papers (2022-09-19T23:05:36Z) - Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of
Demonstrations for Social Navigation [92.66286342108934]
Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a'socially compliant' manner in the presence of other intelligent agents such as humans.
Our dataset contains 8.7 hours, 138 trajectories, 25 miles of socially compliant, human teleoperated driving demonstrations.
arXiv Detail & Related papers (2022-03-28T19:09:11Z) - Intention Aware Robot Crowd Navigation with Attention-Based Interaction
Graph [3.8461692052415137]
We study the problem of safe and intention-aware robot navigation in dense and interactive crowds.
We propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents.
We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios.
arXiv Detail & Related papers (2022-03-03T16:26:36Z) - Cognitive architecture aided by working-memory for self-supervised
multi-modal humans recognition [54.749127627191655]
The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions.
Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task.
One solution is to make robots learn from their first-hand sensory data with self-supervision.
arXiv Detail & Related papers (2021-03-16T13:50:24Z) - LaND: Learning to Navigate from Disengagements [158.6392333480079]
We present a reinforcement learning approach for learning to navigate from disengagements, or LaND.
LaND learns a neural network model that predicts which actions lead to disengagements given the current sensory observation, and then at test time plans and executes actions that avoid disengagements.
Our results demonstrate LaND can successfully learn to navigate in diverse, real world sidewalk environments, outperforming both imitation learning and reinforcement learning approaches.
arXiv Detail & Related papers (2020-10-09T17:21:42Z) - Robot Perception enables Complex Navigation Behavior via Self-Supervised
Learning [23.54696982881734]
We propose an approach to unify successful robot perception systems for active target-driven navigation tasks via reinforcement learning (RL)
Our method temporally incorporates compact motion and visual perception data, directly obtained using self-supervision from a single image sequence.
We demonstrate our approach on two real-world driving dataset, KITTI and Oxford RobotCar, using the new interactive CityLearn framework.
arXiv Detail & Related papers (2020-06-16T07:45:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.