237 citations · 607 across the 11 of their papers we have counts for
11 papers
Gradient-Based Dovetail Joint Shape Optimization for Stiffness
Xingyuan Sun, Chenyue Cai, Ryan P. Adams +1
It is common to manufacture an object by decomposing it into parts that can be assembled. This decomposition is often required by size limits of the machine, the complex structure…
More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites
Xingyuan Sun, Geoffrey Roeder, Tianju Xue +2
In 3D printing, stiff fibers (e.g., carbon fiber) can reinforce thermoplastic polymers with limited stiffness. However, existing commercial digital manufacturing software only prov…
Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower
Jimmy Wu, Xingyuan Sun, Andy Zeng +3
We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a means of efficiently moving scattered objects into a target receptacle. Due to the chaotic nature of aerod…
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate
Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz +1
In design, fabrication, and control problems, we are often faced with the task of synthesis, in which we must generate an object or configuration that satisfies a set of constraint…
Spatial Intention Maps for Multi-Agent Mobile Manipulation
Jimmy Wu, Xingyuan Sun, Andy Zeng +3
The ability to communicate intention enables decentralized multi-agent robots to collaborate while performing physical tasks. In this work, we present spatial intention maps, a new…
Spatial Action Maps for Mobile Manipulation
Jimmy Wu, Xingyuan Sun, Andy Zeng +4
Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e.g., step forward, turn left, turn right, etc.) from im…
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
Runzhe Yang, Xingyuan Sun, Karthik Narasimhan
We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim…
Learning to Infer and Execute 3D Shape Programs
Yonglong Tian, Andrew Luo, Xingyuan Sun +4
Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape struct…
3D Shape Perception from Monocular Vision, Touch, and Shape Priors
Shaoxiong Wang, Jiajun Wu, Xingyuan Sun +4
Perceiving accurate 3D object shape is important for robots to interact with the physical world. Current research along this direction has been primarily relying on visual observat…
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
Xingyuan Sun, Jiajun Wu, Xiuming Zhang +5
We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pix…
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Jiajun Wu, Yifan Wang, Tianfan Xue +3
3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenges for learning-…