{"ID":5552837,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-03T20:14:26.82372516Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00191","arxiv_id":"2607.00191","title":"HydraCollab: Adaptive Collaborative-Perception for Distributed Autonomous Systems","abstract":"Collaborative-perception enables multi-robot systems to enhance situational awareness by sharing perceptual information. Existing collaborative-perception systems face an inherent trade-off between communication bandwidth requirements and perception accuracy, where methods that exchange more information achieve better perception results at the cost of increased communication overhead. However, real-world communication networks impose bandwidth constraints that require minimizing communication overhead without sacrificing perception performance. To address this challenge, we propose HydraCollab, an adaptive collaborative-perception framework that (i) selectively transmits the most informative sensor features and (ii) dynamically employs collaboration strategies (intermediate or late) based on spatial confidence maps. Extensive evaluations on the V2X-R, V2X-Radar and UAV3D-mini datasets demonstrate that HydraCollab achieves the best overall trade-off between accuracy and communication cost among existing collaborative-perception methods. Relative to SOTA Where2comm, HydraCollab uses only 41% of the bandwidth on V2X-R and 26% on V2X-Radar while improving performance by 0.78% and 0.75% respectively. Our code and models are available at https://github.com/AICPS/HydraCollab.","short_abstract":"Collaborative-perception enables multi-robot systems to enhance situational awareness by sharing perceptual information. Existing collaborative-perception systems face an inherent trade-off between communication bandwidth requirements and perception accuracy, where methods that exchange more information achieve better...","url_abs":"https://arxiv.org/abs/2607.00191","url_pdf":"https://arxiv.org/pdf/2607.00191v1","authors":"[\"Luke Chen\",\"Cheng-Ju Wu\",\"David R. Martin\",\"Qilin Ye\",\"Pramod Khargonekar\",\"Mohammad Abdullah Al Faruque\"]","published":"2026-06-30T21:16:43Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.CV\",\"cs.LG\",\"cs.MA\"]","methods":"[]","has_code":false,"code_links":[{"ID":613862,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-02T01:54:51.863792489Z","DeletedAt":null,"paper_id":5552837,"paper_url":"https://arxiv.org/abs/2607.00191","paper_title":"HydraCollab: Adaptive Collaborative-Perception for Distributed Autonomous Systems","repo_url":"https://github.com/AICPS/HydraCollab","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}