arXiv:2606. 17897v1 Announce Type: new Abstract: Long-term human path forecasting in crowds is critical for autonomous moving platforms (like autonomous driving cars and social robots) to avoid collision and make high-quality planning.
arXiv:2607. 07357v1 Announce Type: cross Abstract: Effective social robot navigation requires sensitivity to human behavior, often revealed through subtle skeletal cues like gait and orientation.
The paper introduces Object-Conditioned Social Diffusion (OCSD), a conditional diffusion model that unifies motion history, multi‑person interactions, and object cues for human motion forecasting in complex scenes. OCSD employs an object‑conditioning mechanism that modulates denoising at each timestep, enabling fine‑grained human‑object reasoning, and a social encoder that captures interactions among all humans. Experiments on the Humans in Kitchens (HiK) and HOI‑M3 benchmarks show state‑of‑the‑art performance, reducing two‑second path error by 31.3% on HiK and 33.2% on HOI‑M3 compared to prior work, while producing more realistic long‑term forecasts.
arXiv:2609.13778v1 Announce Type: cross Abstract: Human trajectory prediction requires modeling both individual motion patterns and social interactions among agents. Existing methods have made substa...
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:2609.37476v1 Announce Type: cross Abstract: Training robust social-navigation policies requires simulators with diverse scene layouts, terrain, and human motion, but constructing such environme...
Long-term autonomy in human-populated environments requires anticipating whether and how people will move at times a robot has not yet observed. Existing representations of pedestrian motion face a tr...
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:2609.36852v1 Announce Type: new Abstract: Trajectory prediction is a key component for understanding human behavior patterns in dynamic scenes. Researchers have devoted substantial efforts to m...
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.
Kairos extends a hierarchical 3D scene graph to a 4D scene graph, storing for each voxel a directional mixture and a presence rate. Spectral predictors forecast both the probability of people being present and the full directional distribution of their motion at any future query time. The model supports conditional queries via pairwise flow dependence and provides calibrated credible intervals that tighten as observations accumulate.
arXiv:2608. 07751v1 Announce Type: cross Abstract: Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors.