Pith. sign in

REVIEW 1 cited by

CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1807.07870 v2 pith:S2WAFYNZ submitted 2018-07-19 cs.RO

Tingxiang Fan , Xinjing Cheng , Jia Pan , Dinesh Manocha , Ruigang Yang This is my paper
classification cs.RO
keywords mobilenavigationcrowdmovefourmaplessmethodplatformspolicy
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Navigation is an essential capability for mobile robots. In this paper, we propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and multi-stage) training framework. We optimize a mapless navigation policy with a robust policy gradient algorithm. Our method enables different types of mobile platforms to navigate safely in complex and highly dynamic environments, such as pedestrian crowds. To demonstrate the superiority of our method, we test our methods with four kinds of mobile platforms in four scenarios. Videos are available at https://sites.google.com/view/crowdmove.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Shared Control of Holonomic Wheelchairs through Reinforcement Learning

    cs.RO 2025-07 conditional novelty 6.0 of 10

    An RL policy trained in Isaac Gym and tested in Gazebo and on a real DAA V1 wheelchair translates 2D joystick commands into collision-free 3D motion for a holonomic wheelchair.

Pith tools