13 papers
Foresee: Making Procedural Futures Tangible through Generative Previews
Yusi Sun, Ying Jiang, Jiayin Lu +4
Procedural guidance can show users how to act without explaining what an action enables later. We present Foresee, an interface combining next-step guidance with consequence previe…
Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation
Xincheng He, Siyu Ma, Chang Yu +5
Transferring robotic skills from simulation to reality requires task knowledge that remains usable across differences in perception, dynamics, and embodiment. We introduce Skill2Re…
ArticulateArena: A Metric for Articulated Kinematics
Yumeng He, Yongfei She, Huanyu Chen +6
Modern methods reconstruct or generate simulation-ready articulated objects, predicting not only their geometry but also how their parts are connected and allowed to move. Evaluati…
RoboEdit: Turning Human Manipulation Videos into Scalable Robot Experience
Yaowei Guo, Zeng Tao, Yuxin Jiang +7
Collecting robot hand-object interaction data is costly and embodiment-specific, yet abundant human-object videos remain unusable for robot training. We present RoboEdit, a human-t…
MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation
Yuxin Jiang, Chang Yu, Yunuo Chen +4
Video-world-model policies learn action-relevant representations by predicting future observations. However, they condition on only a short observation window, which renders long-h…
Sparse2Act: Learning Action-Aligned Sparse 3D Representations for Cross-Domain Robot Manipulation
Yu Guo, Chang Yu, Siyu Ma +4
Explicit 3D representations are attractive for manipulation because they expose object shape, workspace geometry, and robot-object relations in metric coordinates. However, sparse…
Fishbone: From One 3D Asset to a Million Controllable Edits
Yumeng He, Xiaoying Wang, Peihao Li +7
Large-scale controllable 3D assets are critical for computer graphics, embodied AI, robotics, and interactive content creation, yet creating diverse 3D assets remains challenging d…
Gradient Descent with Projection Finds Over-Parameterized Neural Networks for Learning Low-Degree Polynomials with Nearly Minimax Optimal Rate
Yingzhen Yang, Ping Li
We study the problem of learning a low-degree spherical polynomial of degree defined on the unit sphere in $\RR^d$ by training an over-parameterized two-layer ne…
SeeClear: Reliable Transparent Object Depth Estimation via Generative Opacification
Xiaoying Wang, Yumeng He, Jingkai Shi +4
Monocular depth estimation remains challenging for transparent objects, where refraction and transmission are difficult to model and break the appearance assumptions used by depth…
M-ABD: Scalable, Efficient, and Robust Multi-Affine-Body Dynamics
Zhiyong He, Dewen Guo, Minghao Guo +6
Simulating large-scale articulated assemblies poses a significant challenge due to the numerical stiffness and geometric complexity of jointed structures. Conventional rigid body s…
MPM Lite: Linear Kernels and Integration without Particles
Xiang Feng, Yunuo Chen, Chang Yu +6
In this paper, we introduce MPM Lite, a new hybrid Lagrangian/Eulerian method that eliminates the need for particle-based quadrature at solve time. Standard MPM practices suffer fr…
VoroLight: Learning Voronoi Surface Meshes via Sphere Intersection
Jiayin Lu, Ying Jiang, Yumeng He +2
Voronoi diagrams naturally produce convex, watertight, and topologically consistent cells, making them an appealing representation for 3D shape reconstruction. However, standard di…
Right-Side-Out: Learning Zero-Shot Sim-to-Real Garment Reversal
Chang Yu, Siyu Ma, Wenxin Du +9
Turning garments right-side out is a challenging manipulation task: it is highly dynamic, entails rapid contact changes, and is subject to severe visual occlusion. We introduce Rig…