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ShuJie Wang

Publications and source records attributed to ShuJie Wang.

2 recordsLinked to original sources
arXiv2026

PrismPlan: A Shared GPU Substrate for Motion Planning Across Paradigms

GPU acceleration has made motion planning one to two orders of magnitude faster, but it has not made the three paradigms---sampling, search, and optimization---comparable to one another: every GPU-accelerated planner is built around a single paradigm, so comparing across libraries also compares their collision checkers. All three, however, bottleneck on the same GPU primitive: batched collision detection and the batched forward kinematics beneath it. PrismPlan builds that primitive once, as a shared substrate of per-sphere collision--kinematics device code that every planner consumes. Because they all execute the same device code, the backend is held fixed by construction and the paradigms can be compared head-to-head. We run six planners over 1800 MotionBenchMaker problems on three robots (7--14 DoF, one a dual-arm whole-body composite) at an equal 2s budget. On the 7-DoF Panda, sampling (pRRTC) is the most reliable paradigm at 98%, while parallel search (wPA*SE) follows within four points at a 3-4 times$ lower median latency, and the local optimizers trail well behind when made to solve from scratch. On the 14-DoF dual-arm composite, sampling still solves about 90% of feasible problems, while no search or optimization planner clears a third of them. Our open-source code is available at https://github.com/XiaoyubotMotionTeam/PrismPlan.

cs.RO↗
arXiv2026

Beyond Relative Geometry: Metric-Aware Geometry Perception for Robotics

Recent embodied models increasingly leverage geometric representations to improve spatial reasoning and robotic manipulation. However, existing reconstruction methods only reconstruct relative geometry with arbitrary scales, causing predicted object dimensions and spatial distances to vary across scenes, viewpoints, and input configurations. This inconsistency prevents geometric perception from being directly aligned with robotic actions defined on the real-world scale. To address this limitation, we propose Metric-Aware Geometry Perception (MAGP), an end-to-end, plug-and-play framework for metric geometry reconstruction that can be seamlessly integrated into robotic policies. At its core, Metric Scale Equivariant Augmentation encourages the model to reconstruct metric geometry from camera parameters and depth observations, ensuring that the reconstructed geometry follows the metric scale specified by observations. Flexible Metric Conditioning further enables MAGP to support arbitrary view counts and combinations of camera and depth inputs, improving robustness to heterogeneous robotic sensing configurations. Together, these designs produce geometrically consistent reconstructions with stable object dimensions and spatial distances across scenes and sensing conditions. Experiments on ETH3D, MegaDepth, and ScanNet++ demonstrate that MAGP maintains strong relative geometry accuracy while reducing the absolute error by over an order of magnitude, from 2.01m to 0.07m. When integrated into multiple robotic policies, MAGP consistently improves performance on LIBERO, RoboTwin, and zero-shot LIBERO-Plus, with gains of up to 6.26% on RoboTwin. These results demonstrate the effectiveness and generalizability of metric geometry for robotic manipulation.

cs.RO↗