arXiv:2607. 01287v1 Announce Type: cross Abstract: Accompanying a group of humans is an essential aspect of developing human-like social cognition in robots.
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The paper introduces a language‑guided approach for robots to join human groups by predicting socially compliant joining poses. It uses recursive spectral partitioning to generate candidate group subsets, ranks them with a language‑conditioned image–geometry model, and then applies a goal predictor that incorporates human‑formation priors to produce a multimodal energy–orientation map of feasible robot poses. Experiments on various group scenarios show competitive grounding accuracy, superior joining‑pose prediction, and successful real‑robot demonstrations in both static and dynamic settings.
arXiv:2609.40245v2 Announce Type: cross Abstract: Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-...
arXiv:2607. 10991v1 Announce Type: cross Abstract: As mobile robots become more integrated into everyday human environments, social robot navigation is becoming essential for ensuring human comfort, safety, and trust.
arXiv:2607. 07021v1 Announce Type: new Abstract: Humans continuously coordinate with others in dynamic interactions, often through implicit, hard-to-quantify social norms that act as shared tacit expectations among interacting agents.
arXiv:2503. 14229v4 Announce Type: replace Abstract: Vision-and-Language Navigation (VLN) has been studied mainly in either discrete or continuous spaces, with little attention to dynamic, crowded environments.
arXiv:2609.23997v1 Announce Type: cross Abstract: Multi-robot collaboration could enable more efficient and scalable solutions to complex robotic tasks, but collaboration under partial observability...