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Semantic-based Loco-Manipulation for Human-Robot Collaboration in Industrial Environments

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arxiv 2312.14487 v1 pith:7Z7K7HWA submitted 2023-12-22 cs.RO

Semantic-based Loco-Manipulation for Human-Robot Collaboration in Industrial Environments

classification cs.RO
keywords environmenttextitrobotautonomybringcollaborationenvironmentsexecution
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Robots with a high level of autonomy are increasingly requested by smart industries. A way to reduce the workers' stress and effort is to optimize the working environment by taking advantage of autonomous collaborative robots. A typical task for Human-Robot Collaboration (HRC) which improves the working setup in an industrial environment is the \textit{"bring me an object please"} where the user asks the collaborator to search for an object while he/she is focused on something else. As often happens, science fiction is ahead of the times, indeed, in the \textit{Iron Man} movie, the robot \textit{Dum-E} helps its creator, \textit{Tony Stark}, to create its famous armours. The ability of the robot to comprehend the semantics of the environment and engage with it is valuable for the human execution of more intricate tasks. In this work, we reproduce this operation to enable a mobile robot with manipulation and grasping capabilities to leverage its geometric and semantic understanding of the environment for the execution of the \textit{Bring Me} action, thereby assisting a worker autonomously. Results are provided to validate the proposed workflow in a simulated environment populated with objects and people. This framework aims to take a step forward in assistive robotics autonomy for industries and domestic environments.

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Cited by 1 Pith paper

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  1. Robotic Contextual Awareness for Human-Robot Collaboration and Environmental Understanding

    cs.RO 2026-07 conditional novelty 6.0

    Novel person re-identification with continual adaptation plus submap LiDAR SLAM, ground-aware filtering, Gaussian Scan Context, and multi-modal semantic mapping improve robotic contextual awareness for HRC and navigation.