The paper introduces 3DWay, a method that predicts 3D-consistent waypoints for robot manipulation by first generating multi‑view consistent 2D waypoints and then triangulating them. This approach addresses the 3D ambiguity inherent in 2D trajectory predictions and leverages pretrained vision‑language models to provide explicit 3D motion specifications. Experiments demonstrate that 3DWay improves 3D spatial grounding and vision‑language reasoning, enhancing generalization for robot manipulation tasks.
By Ziqin Huang, Yingyue Li, Chenyangguang Zhang, Ruida Zhang, Yuxin Chen, Gu Wang, Xingyu Liu, Masayoshi Tomizuka, Xiangyang Ji
arXiv:2606. 31329v1 Announce Type: cross Abstract: Hierarchical Vision-Language-Action (VLA) models decouple high-level planning from low-level control to improve generalization in robot manipulation.
By Dongyoon Hwang, Byungkun Lee, Dongjin Kim, Hyojin Jang, Hoiyeong Jin, Jueun Mun, Minho Park, Hojoon Lee, Hyunseung Kim, Jaegul Choo
arXiv:2607. 04714v1 Announce Type: cross Abstract: Learning motion latents for robotic manipulation heavily relies on extracting motion patterns from visual sequences, yet effective action abstractions require understanding three-dimensional geometric transformations.
By Yunchao Zhang, Yijia Weng, Ruizhe Liu, Ming Hu, Leonidas Guibas, Yanchao Yang
arXiv:2606. 03943v1 Announce Type: cross Abstract: Video-Action Models (VAMs) leverage the broad visual dynamics captured by pre-trained video diffusion models, offering a promising path toward generalizable robot manipulation.
By Mutian Tong, Han Jiang, Qiao Feng, Lingjie Liu, Jiatao Gu
arXiv:2609.01059v1 Announce Type: new
Abstract: As Vision-Language Models (VLMs) tackle dynamic 3D spatial reasoning, ego-motion perception becomes essential to resolve monocular scale ambiguity. How...
By Jiayu Ding, Zhuodong Liu, Lei Zhang, Manyu Xiong, Hongbo Jin, Haoran Tang, Hongbo Zhang, Changen Zhu, Wenbo Xing
arXiv:2506.11261v2 Announce Type: replace-cross
Abstract: Vision-language-action (VLA) models have shown promising progress in robotic manipulation. However, directly mapping visual observations and...
By Shizhe Chen, Ricardo Garcia, Paul Pacaud, Cordelia Schmid
FOCAL‑VLA is a framework that improves vision‑language‑action models by combining subtask‑guided geometry distillation with implicit world modeling. It transfers geometric knowledge from VGGT to focus on subtask‑relevant image regions and uses Track4World features to capture future 3D evolution, guiding action generation without running these models at inference time. Experiments demonstrate that FOCAL‑VLA outperforms baselines on both simulation benchmarks and real‑world manipulation tasks.
By Zhiyuan Gao, Di Wen, Yanxiang Zhan, Mohammad Khoshnazar, Jeroen Sch\"afer, Kunyu Peng, Michael Beetz
arXiv:2606.18558v2 Announce Type: replace
Abstract: Motion forecasting is central to visual intelligence: agents must anticipate how objects will move in order to plan actions, reason about physical...
By Jianing Zhang, Chenhao Zheng, Yajun Yang, Max Argus, Rustin Soraki, Winson Han, Taira Anderson, Chun-Liang Li, Shuo Liu, Jiafei Duan, Zhongzheng Ren, Jieyu Zhang, Ranjay Krishna
arXiv:2607. 10706v1 Announce Type: cross Abstract: The action space poses a major challenge in robot learning, since it is often high-dimensional, can span long time horizons, and frequently admits multi-modal optimal solutions.
By Haojie Huang, Zhang Ye, Linfeng Zhao, Boce Hu, Mingxi Jia, Yu Qi, Ahmed Agha, Dian Wang, Robert Platt, Robin Walters
arXiv:2610.29866v1 Announce Type: new
Abstract: Geometric structure is essential for robotic manipulation and understanding the 3D world. Most VLA models rely on 2D representations of current observa...
By Qinfan Zhang, Yifan Luo, Haoyuan Shi, Guanlin Liu, Han Dong, Haozhe Shan, Yong Dai, Xiangcong Ren, Xiaozhu Ju
The action space poses a major challenge in robot learning, since it is often high-dimensional, can span long time horizons, and frequently admits multi-modal optimal solutions. A good choice of action representation and loss function can help to address these concerns, but there are often trade offs.
KeyGen is a framework that learns canonical 3D keypoints from point clouds to create structured, object‑centric representations for policy learning in robotic manipulation. By conditioning a visuomotor diffusion policy on these keypoints and object geometry, it predicts full manipulation trajectories that maintain geometric correspondence across different object instances. Experiments on a photorealistic simulation benchmark with three tasks show that KeyGen outperforms prior methods on both seen and unseen objects, scales with more demonstrations, remains robust to rescaling, and performs well in real‑world manipulation.
By Shuxin Cao, Liquan Wang, Masoud Moghani, Benjamin Joffe, Animesh Garg