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.
By Iacopo Catalano, Julio A. Placed, Javier Civera, Jorge Pe\~na Queralta
arXiv:2606. 20209v1 Announce Type: cross Abstract: Joint spatial and temporal understanding of 3D scenes is a crucial requirement for robots deployed in everyday household environments.
By Francesco Argenziano, Miguel Saavedra-Ruiz, Sacha Morin, Charlie Gauthier, Daniele Nardi, Liam Paull
PV-WM is a history‑only world model that jointly predicts pedestrian root motion, 15‑joint articulation, and vehicle kinematic states in a synchronized heterogeneous state. It uses recurrent updates to generate pedestrian and vehicle motion chunks, reconstructing vehicle boxes from predicted center, heading, and observed extent, and recomputes pedestrian‑vehicle geometry after each transition. Compared to a one‑shot predictor, PV‑WM reduces Root ADE by 12.7% and MPJPE by 14.8%, and across 824 Waymo contexts it lowers Root ADE by 5.2%, MPJPE by 7.6%, P‑V distance error by 11.9%, and oriented‑box closest‑approach error by 5.8%, while using 57.1% fewer parameters, 96.5% fewer FLOPs, and 25.5% lower p95 latency.
By Haozhuang Chi, Jingsong Liang, Ziying Song, Lei Yang, Shihao Li, Haoruo Zhang, Chen Lv
arXiv:2609.08636v1 Announce Type: cross
Abstract: Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize...
By Qiaohui Chu, Haoyu Zhang, Meng Liu, Haoxiang Shi, Dongmei Jiang, Liqiang Nie
MamMA is a pedestrian trajectory prediction algorithm that leverages LiDAR-generated occupancy maps and egocentric vision sensor data. It partitions the occupancy map into patches to extract obstacle features and incorporates pedestrian awareness states, which influence perception and speed. Using a Mamba-based model, MamMA predicts future trajectories and outperforms state‑of‑the‑art methods on multiple benchmark datasets.
By Juncen Long, Xiaofeng Jin, Gianluca Bardaro, Simone Mentasti, Matteo Matteucci
arXiv:2606. 18824v1 Announce Type: cross Abstract: Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian.
By Yuxuan Xie, Nicolas Pugeault, Chongfeng Wei, Hubert P. H. Shum, Edmond S. L. Ho
arXiv:2605.25059v4 Announce Type: replace
Abstract: Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Em...
By Ruoyu Wang, Yong Liu, Jiahan Li, Sheng Tao, Yuhang Lin, Yukai Ma
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...
By Jiaming Wang, Duc Thang Nguyen, Jizhuo Chen, Volodymyr Shcherbyna, Diwen Liu, Zhengcheng Shen, Harold Soh
Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at signalized intersections. Learned trajectory predictors capture richer interactions but are short-horizon and tend to be unstable when run in closed loop.
The paper introduces Feel‑WM, an off‑road navigation world model that incorporates proprioceptive data to predict both visual scenes and the robot’s physical sensations such as slip, tilt, and shake. By learning a future proprioceptive state and failure risk from the robot’s own experience, the model can evaluate planned trajectories using a separable score that balances goal similarity with predicted failure risk. Experiments on real and simulated off‑road data show that Feel‑WM outperforms visual‑only models in both open‑loop planning and closed‑loop navigation for wheeled and legged robots, and it successfully guides a Husky robot around rough terrain on mountain trails where an end‑to‑end policy fails.
By E-In Son, Dong-Wook Kim, Ji-Hoon Hwang, Kangsun Lee, Jisung Bae, Jung-Taak Kim, Seung-Woo Seo
arXiv:2607. 08436v1 Announce Type: cross Abstract: Egocentric human data offers scalable supervision for robot manipulation.
By Baoyu Li, Xinchen Yin, Mengying Lin, Yixin Zhang, Danfei Xu
arXiv:2606. 12603v1 Announce Type: cross Abstract: Autonomous long-horizon sidewalk navigation is essential for micro-mobility applications such as robotic food delivery and assistive electronic wheelchairs.
By Honglin He, Zhizheng Liu, Yukai Ma, Bolei Zhou