Follow-Me in Micro-Mobility with End-to-End Imitation Learning

• URL: http://arxiv.org/abs/2511.05158v1
• Date: Fri, 07 Nov 2025 11:24:20 GMT
• Title: Follow-Me in Micro-Mobility with End-to-End Imitation Learning
• Abstract summary: We show how imitation learning delivers smoother and overall better controllers, versus previously used manually-tuned controllers.<n>This paper analyzes different neural network architectures for end-to-end control and demonstrates their usability in real-world production-level deployments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Autonomous micro-mobility platforms face challenges from the perspective of the typical deployment environment: large indoor spaces or urban areas that are potentially crowded and highly dynamic. While social navigation algorithms have progressed significantly, optimizing user comfort and overall user experience over other typical metrics in robotics (e.g., time or distance traveled) is understudied. Specifically, these metrics are critical in commercial applications. In this paper, we show how imitation learning delivers smoother and overall better controllers, versus previously used manually-tuned controllers. We demonstrate how DAAV's autonomous wheelchair achieves state-of-the-art comfort in follow-me mode, in which it follows a human operator assisting persons with reduced mobility (PRM). This paper analyzes different neural network architectures for end-to-end control and demonstrates their usability in real-world production-level deployments.

Related papers

• 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)
• ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation [55.467742403416175]
  We introduce a physics-driven neural algorithm that translates large-scale motion capture to humanoid embodiments.<n>We learn a unified multimodal controller that supports both dense references and sparse task specifications.<n>Results show that ULTRA generalizes to autonomous, goal-conditioned whole-body loco-manipulation from egocentric perception.
  arXiv  Detail & Related papers  (2026-03-03T18:59:29Z)
• MobileManiBench: Simplifying Model Verification for Mobile Manipulation [70.30578259859512]
  MobileManiBench is a large-scale benchmark for mobile-based robotic manipulation.<n>MobileManiBench features 2 mobile platforms (parallel-gripper and dexterous-hand robots), 2 synchronized cameras (head and right wrist), 630 objects in 20 categories, 5 skills (open, close, pull, push, pick) with over 100 tasks performed in 100 realistic scenes, yielding 300K trajectories.
  arXiv  Detail & Related papers  (2026-02-05T02:49:52Z)
• Watch and Learn: Learning to Use Computers from Online Videos [50.10702690339142]
  Watch & Learn (W&L) is a framework that converts human demonstration videos readily available on the Internet into executable UI trajectories at scale.<n>We develop an inverse dynamics labeling pipeline with task-aware video retrieval, generate over 53k high-quality trajectories from raw web videos.<n>These results highlight web-scale human demonstration videos as a practical and scalable foundation for advancing CUAs towards real-world deployment.
  arXiv  Detail & Related papers  (2025-10-06T10:29:00Z)
• Shared Control of Holonomic Wheelchairs through Reinforcement Learning [1.4970676989901233]
  State-of-the-art work showed the potential of shared control in improving safety in navigation for non-holonomic robots.<n>We propose a reinforcement learning-based method, which takes a 2D user input and outputs a 3D motion.<n>We show that our method ensures collision-free navigation while smartly orienting the wheelchair and showing better or competitive smoothness.
  arXiv  Detail & Related papers  (2025-07-22T22:31:11Z)
• Towards Autonomous Micromobility through Scalable Urban Simulation [52.749987132021324]
  Current micromobility depends mostly on human manual operation (in-person or remote control)<n>In this work, we present a scalable urban simulation solution to advance autonomous micromobility.
  arXiv  Detail & Related papers  (2025-05-01T17:52:29Z)
• MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility [52.0930915607703]
  Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. We present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research.
  arXiv  Detail & Related papers  (2024-07-11T17:56:49Z)
• Enhancing End-to-End Autonomous Driving with Latent World Model [78.22157677787239]
  We propose a novel self-supervised learning approach using the LAtent World model (LAW) for end-to-end driving.<n> LAW predicts future scene features based on current features and ego trajectories.<n>This self-supervised task can be seamlessly integrated into perception-free and perception-based frameworks.
  arXiv  Detail & Related papers  (2024-06-12T17:59:21Z)
• Learning to navigate efficiently and precisely in real environments [14.52507964172957]
  Embodied AI literature focuses on end-to-end agents trained in simulators like Habitat or AI-Thor. In this work we explore end-to-end training of agents in simulation in settings which minimize the sim2real gap.
  arXiv  Detail & Related papers  (2024-01-25T17:50:05Z)
• Learning and Adapting Agile Locomotion Skills by Transferring Experience [71.8926510772552]
  We propose a framework for training complex robotic skills by transferring experience from existing controllers to jumpstart learning new tasks. We show that our method enables learning complex agile jumping behaviors, navigating to goal locations while walking on hind legs, and adapting to new environments.
  arXiv  Detail & Related papers  (2023-04-19T17:37:54Z)
• RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring [0.5156484100374059]
  We introduce a new approach to person following for assistance and monitoring. Our methodology exploits an omnidirectional robotic platform to detach the computation of linear and angular velocities. We evaluate our navigation system on a real omnidirectional platform in various indoor scenarios.
  arXiv  Detail & Related papers  (2022-11-09T16:11:41Z)
• Learning Social Navigation from Demonstrations with Conditional Neural Processes [2.627046865670577]
  This paper presents a data-driven navigation architecture that uses Conditional Neural Processes to learn global and local controllers of the mobile robot from observations. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data.
  arXiv  Detail & Related papers  (2022-10-07T14:37:56Z)
• Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study [9.118706387430883]
  This paper presents a novel reinforcement learning framework to train a personalized navigation controller. The conducted user study provides evidence that our personalized approach significantly outperforms classical approaches. We transfer the navigation controller without loss in performance to a real robot.
  arXiv  Detail & Related papers  (2022-03-28T13:31:12Z)
• Learning to Move with Affordance Maps [57.198806691838364]
  The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
  arXiv  Detail & Related papers  (2020-01-08T04:05:11Z)

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.