The paper introduces Constraint-Referenced Strategy (CRS), a method that evaluates manipulation policies by projecting the commanded end-effector twist onto an object's joint axis, thereby distinguishing progress along the joint from off-constraint motion. CRS requires a joint model and a scripted controller but no demonstrations or learned judges. Experiments on 74 simulated articulated objects show that CRS can identify distinct signatures of policy failures and highlight significant off-axis rotation in a door-trained policy compared to a drawer-trained one.
By Baptiste Bonin, Caro Strickland, Audrey Durand
arXiv:2610.08446v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) have made significant progress in visual understanding, but precise 3D spatial reasoning integrated with physi...
By Zhiyuan Qi, Jierui Li, Yifan Shen, Cheng Qian, Jiateng Liu
The paper reports a 2nd place solution for the HANDS 2026 Dexterous Grasp Motion track, targeting the 12‑DoF LinkerHand O6. The method warps a single successful GraspM3 demonstration into a 12‑D trajectory, avoiding step‑by‑step policy generation, and is trained with one‑step PPO across 4,824 objects. It achieved 94.61% success on the easy track and 57.18% on the hard track of the private test set.
By Muneeb A. Khan, Woojin Kim, Shinwoo Kim, Muhammad Munsif, Binod Bhattarai, Seungryul Baek
The paper introduces Artisan, a multi‑agent pipeline that builds on Articraft to generate physically grounded articulated 3D assets. Artisan incorporates physical simulation feedback to ensure attributes such as collision‑free motion, stability under gravity, and accurate mass and material properties. The authors also present a unified benchmark covering spatial fidelity, physical plausibility, downstream task performance, and overall quality, evaluated on 100 reference assets and 100 stability prompts, where Artisan shows competitive spatial fidelity and the lowest collision and highest stability rates among tested models.
By Xudong Cai, Ruijie Zhu, Haoliang Shang, Ruining Li, Marc Pollefeys, Shangzhe Wu, Andrea Vedaldi, Chuanxia Zheng
Multimodal large language models (MLLMs) have made significant progress in visual understanding, but precise 3D spatial reasoning integrated with physical environment remains difficult. Furniture asse...
Masked Generative Motion Planning (MGMP) extends learned trajectory priors by using a masked generative transformer to produce discrete trajectory candidates in parallel and a Geometry‑Guided Token Search (GGTS) to target and evaluate edits based on scene geometry. This approach turns refinement into an efficient search over discrete motion alternatives, allowing route‑level restructuring rather than only local deformation. MGMP achieves high success rates—96% on Ring Maze and 82% repair success on Controlled Route Invalidation—outperforming external baselines by 23–25 percentage points and generalizes to unseen layouts, additional obstacles, unseen geometries, single‑ and dual‑arm planning, and real‑world Baxter tasks.
By Lipeng Zhuang, Yingdong Ru, Shiyu Fan, Edmond S. L. Ho, Gerardo Aragon Camarasa, Paul Henderson
arXiv:2605. 18010v2 Announce Type: replace Abstract: Acquisition and creation of 3D assets have been largely view- or appearance-driven.
By Mingrui Zhao, Sai Raj Kishore Perla, Kai Wang, Sauradip Nag, Duc Anh Nguyen, Jiayi Peng, Ruiqi Wang, Angel X. Chang, Manolis Savva, Ali Mahdavi-Amiri, Hao Zhang
arXiv:2608.20699v1 Announce Type: new
Abstract: Animating articulated 3D meshes via text requires satisfying strict kinematic constraints, modeling causal interactions between parts, and achieving in...
By Chunyu Zou, Peng Dai, Yi-Hua Huang, Ze Yuan, Jingwei Huang, Yeming Yao, Xiaojuan Qi
arXiv:2609.37067v1 Announce Type: cross
Abstract: Visually plausible articulated assets may still fail during contact interactions or exhibit inaccurate motion. We present FACT (Fidelity-Aware Constr...
By Kuixiang Shao, Chuansen Nie, Yinuo Bai, Jiayuan Gu, Jingyi Yu
arXiv:2608. 09296v1 Announce Type: new Abstract: A CAD model is not engineering-grade merely because it looks correct.
By Harmanjot Singh, Abhra Dubey, Jorge Alejandro Amador Herrera
arXiv:2603.19305v3 Announce Type: replace-cross
Abstract: Humanoid robots are expected to execute agile and expressive whole-body motions in real-world settings. Existing text-to-motion generation mo...
By Jiacheng Bao, Haoran Yang, Yucheng Xin, Junhong Liu, Yuecheng Xu, Han Liang, Pengfei Han, Xiaoguang Ma, Dong Wang, Bin Zhao
The paper introduces Watch, Infer, Coordinate, a benchmark and inference method for robots to deduce a partner’s physical constraints by observing joint behavior in physically coupled manipulation tasks. It demonstrates that understanding these constraints enables zero‑shot coordination on new tasks, achieving performance close to an oracle with true constraint knowledge. The study focuses on scenarios where a robot’s limitations may be unknown to its partner, such as hardware degradation or actuator faults.
By Suyu Ye, Zheyuan Zhang, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Tianmin Shu, Homanga Bharadhwaj, Nakul Agarwal