7 papers
Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints
Qiwei Du, Zitong Zhan, Shaoshu Su +7
Task planning often suffers from severe efficiency bottlenecks when robots must reason over long-horizon action sequences under complex logical constraints, including object afford…
Bundle Adjustment in the Eager Mode
Zitong Zhan, Huan Xu, Zihang Fang +3
Bundle adjustment (BA) is a critical technique in various robotic applications such as simultaneous localization and mapping (SLAM), augmented reality (AR), and photogrammetry. BA…
Data Warmup: Complexity-Aware Curricula for Efficient Diffusion Training
Jinhong Lin, Pan Wang, Zitong Zhan +2
A key inefficiency in diffusion training occurs when a randomly initialized network, lacking visual priors, encounters gradients from the full complexity spectrum--most of which it…
InstantSfM: Towards GPU-Native SfM for the Deep Learning Era
Jiankun Zhong, Zitong Zhan, Quankai Gao +6
Structure-from-Motion (SfM) is a fundamental technique for recovering camera poses and scene structure from multi-view imagery, serving as a critical upstream component for applica…
Fast Task Planning with Neuro-Symbolic Relaxation
Qiwei Du, Bowen Li, Yi Du +5
Real-world task planning requires long-horizon reasoning over large sets of objects with complex relationships and attributes, leading to a combinatorial explosion for classical sy…
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
Chen Wang, Kaiyi Ji, Junyi Geng +16
Data-driven methods such as reinforcement and imitation learning have achieved remarkable success in robot autonomy. However, their data-centric nature still hinders them from gene…