FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalization

• URL: http://arxiv.org/abs/2303.06872v4
• Date: Tue, 25 Jul 2023 07:07:12 GMT
• Title: FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalization
• Abstract summary: We focus on end-to-end relocalization of serving robots to address the problem. It is to predict robot pose directly from only the onboard sensor data using neural networks. In the proposed method, the multi-head self-attention complements different types of information captured by the two sensors to regress the robot pose.
• Score: 1.9766522384767222
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As technology advances in autonomous mobile robots, mobile service robots have been actively used more and more for various purposes. Especially, serving robots have been not surprising products anymore since the COVID-19 pandemic. One of the practical problems in operating a serving robot is that it often fails to estimate its pose on a map that it moves around. Whenever the failure happens, servers should bring the serving robot to its initial location and reboot it manually. In this paper, we focus on end-to-end relocalization of serving robots to address the problem. It is to predict robot pose directly from only the onboard sensor data using neural networks. In particular, we propose a deep neural network architecture for the relocalization based on camera-2D LiDAR sensor fusion. We call the proposed method FusionLoc. In the proposed method, the multi-head self-attention complements different types of information captured by the two sensors to regress the robot pose. Our experiments on a dataset collected by a commercial serving robot demonstrate that FusionLoc can provide better performances than previous end-to-end relocalization methods taking only a single image or a 2D LiDAR point cloud as well as a straightforward fusion method concatenating their features.

Related papers

• Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data [60.94418315274988]
  Ego2Robot supports both curated and in-the-wild videos, producing 18,561 hours of robot training data spanning 15 robot morphologies.<n>Joint pretraining on Ego2Robot-synthesized and robot data consistently improves out-of-distribution generalization across multiple types.
  arXiv  Detail & Related papers  (2026-08-03T17:52:26Z)
• RobotSeg: A Model and Dataset for Segmenting Robots in Image and Video [56.9581053843815]
  We introduce RobotSeg, a foundation model for robot segmentation in image and video.<n>It addresses the lack of adaptation to articulated robots, reliance on manual prompts, and the need for per-frame training mask annotations.<n>It achieves state-of-the-art performance on both images and videos.
  arXiv  Detail & Related papers  (2025-11-28T07:51:02Z)
• EgoZero: Robot Learning from Smart Glasses [54.6168258133554]
  EgoZero learns robust manipulation policies from human demonstrations captured with Project Aria smart glasses.<n>We deploy EgoZero policies on a Franka Panda robot and demonstrate zero-shot transfer with 70% success rate over 7 manipulation tasks.<n>Our results suggest that in-the-wild human data can serve as a scalable foundation for real-world robot learning.
  arXiv  Detail & Related papers  (2025-05-26T17:59:17Z)
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots [133.23509142762356]
  General-purpose robots need a versatile body and an intelligent mind. Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist autonomy. We introduce GR00T N1, an open foundation model for humanoid robots.
  arXiv  Detail & Related papers  (2025-03-18T21:06:21Z)
• 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)
• 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)
• 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)
• Robot Learning with Sensorimotor Pre-training [98.7755895548928]
  We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
  arXiv  Detail & Related papers  (2023-06-16T17:58:10Z)
• External Camera-based Mobile Robot Pose Estimation for Collaborative Perception with Smart Edge Sensors [22.5939915003931]
  We present an approach for estimating a mobile robot's pose w.r.t. the allocentric coordinates of a network of static cameras using multi-view RGB images. The images are processed online, locally on smart edge sensors by deep neural networks to detect the robot. With the robot's pose precisely estimated, its observations can be fused into the allocentric scene model.
  arXiv  Detail & Related papers  (2023-03-07T11:03:33Z)
• Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision [72.4735163268491]
  Commercial and industrial deployments of robot fleets often fall back on remote human teleoperators during execution. We formalize the Interactive Fleet Learning (IFL) setting, in which multiple robots interactively query and learn from multiple human supervisors. We propose Fleet-DAgger, a family of IFL algorithms, and compare a novel Fleet-DAgger algorithm to 4 baselines in simulation.
  arXiv  Detail & Related papers  (2022-06-29T01:23:57Z)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)
• 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)
• Know Thyself: Transferable Visuomotor Control Through Robot-Awareness [22.405839096833937]
  Training visuomotor robot controllers from scratch on a new robot typically requires generating large amounts of robot-specific data. We propose a "robot-aware" solution paradigm that exploits readily available robot "self-knowledge" Our experiments on tabletop manipulation tasks in simulation and on real robots demonstrate that these plug-in improvements dramatically boost the transferability of visuomotor controllers.
  arXiv  Detail & Related papers  (2021-07-19T17:56:04Z)
• Single-view robot pose and joint angle estimation via render & compare [40.05546237998603]
  We introduce RoboPose, a method to estimate the joint angles and the 6D camera-to-robot pose of a known articulated robot from a single RGB image. This is an important problem to grant mobile and itinerant autonomous systems the ability to interact with other robots.
  arXiv  Detail & Related papers  (2021-04-19T14:48:29Z)
• Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments [0.0]
  We present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner. The input for the robot is only the fused data from a 2D laser scanner and a RGB-D camera as well as the orientation to the goal. The output actions of an Asynchronous Advantage Actor-Critic network (GA3C) are the linear and angular velocities for the robot.
  arXiv  Detail & Related papers  (2020-05-28T09:15:14Z)
• Morphology-Agnostic Visual Robotic Control [76.44045983428701]
  MAVRIC is an approach that works with minimal prior knowledge of the robot's morphology. We demonstrate our method on visually-guided 3D point reaching, trajectory following, and robot-to-robot imitation.
  arXiv  Detail & Related papers  (2019-12-31T15:45:10Z)

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.