Is Robot Labor Labor? Delivery Robots and the Politics of Work in Public Space
- URL: http://arxiv.org/abs/2602.20180v1
- Date: Wed, 18 Feb 2026 20:57:58 GMT
- Title: Is Robot Labor Labor? Delivery Robots and the Politics of Work in Public Space
- Authors: EunJeong Cheon, Do Yeon Shin,
- Abstract summary: As sidewalk delivery robots become increasingly integrated into urban life, this paper begins with a critical provocation: Is robot labor labor?<n>We argue that delivery robots do not replace labor but reconfigure it--rendering some forms more visible (robotic performance) while obscuring others (human and institutional support)<n>We contribute a conceptual reframing of robot labor as a collective assemblage, empirical insights into South Korea's smart-city automation, and a call for HRI to engage more deeply with labor and spatial politics to better theorize public-facing robots.
- Score: 4.046407662099088
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As sidewalk delivery robots become increasingly integrated into urban life, this paper begins with a critical provocation: Is robot labor labor? More than a rhetorical question, this inquiry invites closer attention to the social and political arrangements that robot labor entails. Drawing on ethnographic fieldwork across two smart-city districts in Seoul, we examine how delivery robot labor is collectively sustained. While robotic actions are often framed as autonomous and efficient, we show that each successful delivery is in fact a distributed sociotechnical achievement--reliant on human labor, regulatory coordination, and social accommodations. We argue that delivery robots do not replace labor but reconfigure it--rendering some forms more visible (robotic performance) while obscuring others (human and institutional support). Unlike industrial robots, delivery robots operate in shared public space, engage everyday passersby, and are embedded in policy and progress narratives. In these spaces, we identify "robot privilege"--humans routinely yielding to robots--and distinct perceptions between casual observers ("cute") and everyday coexisters ("admirable"). We contribute a conceptual reframing of robot labor as a collective assemblage, empirical insights into South Korea's smart-city automation, and a call for HRI to engage more deeply with labor and spatial politics to better theorize public-facing robots.
Related papers
- Choreographing Trash Cans: On Speculative Futures of Weak Robots in Public Spaces [0.0]
This paper explores mobile robots that encourage posthuman collaboration rather than managing environments independently.<n>We examine the workings of "weak robots" by queering notions of function and ability.<n>We introduce two speculative design fiction vignettes that describe choreographies of such robots in future urban spaces.
arXiv Detail & Related papers (2025-09-01T17:27:43Z) - Generalizable Humanoid Manipulation with 3D Diffusion Policies [66.78220965526732]
We build a real-world robotic system to address the problem of autonomous manipulation by humanoid robots.<n>Our system is mainly an integration of 1) a whole-upper-body robotic teleoperation system to acquire human-like robot data, and 2) a 25-DoF humanoid robot platform with a height-adjustable cart and a 3D LiDAR sensor.<n>We show that using only data collected in one scene and with only onboard computing, a full-sized humanoid robot can autonomously perform skills in diverse real-world scenarios.
arXiv Detail & Related papers (2024-10-14T17:59:00Z) - Controlling diverse robots by inferring Jacobian fields with deep networks [48.279199537720714]
Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics.<n>We introduce a method that uses deep neural networks to map a video stream of a robot to its visuomotor Jacobian field.<n>Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot.
arXiv Detail & Related papers (2024-07-11T17:55:49Z) - HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation [50.616995671367704]
We present a high-dimensional, simulated robot learning benchmark, HumanoidBench, featuring a humanoid robot equipped with dexterous hands.
Our findings reveal that state-of-the-art reinforcement learning algorithms struggle with most tasks, whereas a hierarchical learning approach achieves superior performance when supported by robust low-level policies.
arXiv Detail & Related papers (2024-03-15T17:45:44Z) - Correspondence learning between morphologically different robots via
task demonstrations [2.1374208474242815]
We propose a method to learn correspondences among two or more robots that may have different morphologies.
A fixed-based manipulator robot with joint control and a differential drive mobile robot can be addressed within the proposed framework.
We provide a proof-of-the-concept realization of correspondence learning between a real manipulator robot and a simulated mobile robot.
arXiv Detail & Related papers (2023-10-20T12:42:06Z) - ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space [9.806227900768926]
This paper introduces a novel deep-learning approach for human-to-robot motion.
Our method does not require paired human-to-robot data, which facilitates its translation to new robots.
Our model outperforms existing works regarding human-to-robot similarity in terms of efficiency and precision.
arXiv Detail & Related papers (2023-09-11T08:55:04Z) - Asch Meets HRI: Human Conformity to Robot Groups [0.9350546589421261]
We present a research outline that aims at investigating group dynamics and peer pressure in the context of industrial robots.
We are interested in highlighting the effects of group size, perceived robot credibility, psychological stress, and peer pressure in the context of industrial robots.
arXiv Detail & Related papers (2023-08-25T11:14:24Z) - Giving Robots a Hand: Learning Generalizable Manipulation with
Eye-in-Hand Human Video Demonstrations [66.47064743686953]
Eye-in-hand cameras have shown promise in enabling greater sample efficiency and generalization in vision-based robotic manipulation.
Videos of humans performing tasks, on the other hand, are much cheaper to collect since they eliminate the need for expertise in robotic teleoperation.
In this work, we augment narrow robotic imitation datasets with broad unlabeled human video demonstrations to greatly enhance the generalization of eye-in-hand visuomotor policies.
arXiv Detail & Related papers (2023-07-12T07:04:53Z) - REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy
Transfer [57.045140028275036]
We consider the problem of transferring a policy across two different robots with significantly different parameters such as kinematics and morphology.
Existing approaches that train a new policy by matching the action or state transition distribution, including imitation learning methods, fail due to optimal action and/or state distribution being mismatched in different robots.
We propose a novel method named $REvolveR$ of using continuous evolutionary models for robotic policy transfer implemented in a physics simulator.
arXiv Detail & Related papers (2022-02-10T18:50:25Z) - Design and Development of Autonomous Delivery Robot [0.16863755729554888]
We present an autonomous mobile robot platform that delivers the package within the VNIT campus without any human intercommunication.
The entire pipeline of an autonomous robot working in outdoor environments is explained in this thesis.
arXiv Detail & Related papers (2021-03-16T17:57:44Z) - Show Me What You Can Do: Capability Calibration on Reachable Workspace
for Human-Robot Collaboration [83.4081612443128]
We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth.
We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
arXiv Detail & Related papers (2021-03-06T09:14:30Z)
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.