arXiv subjects

Yandong Luo

Publications and source records attributed to Yandong Luo.

3 recordsLinked to original sources

arXiv2025

ShelfGaussian: Shelf-Supervised Open-Vocabulary Gaussian-based 3D Scene Understanding

We introduce ShelfGaussian, an open-vocabulary multi-modal Gaussian-based 3D scene understanding framework supervised by off-the-shelf vision foundation models (VFMs). Gaussian-based methods have demonstrated superior performance and computational efficiency across a wide range of scene understanding tasks. However, existing methods either model objects as closed-set semantic Gaussians supervised by annotated 3D labels, neglecting their rendering ability, or learn open-set Gaussian representations via purely 2D self-supervision, leading to degraded geometry and limited to camera-only settings. To fully exploit the potential of Gaussians, we propose a Multi-Modal Gaussian Transformer that enables Gaussians to query features from diverse sensor modalities, and a Shelf-Supervised Learning Paradigm that efficiently optimizes Gaussians with VFM features jointly at 2D image and 3D scene levels. We evaluate ShelfGaussian on various perception and planning tasks. Experiments on Occ3D-nuScenes demonstrate its state-of-the-art zero-shot semantic occupancy prediction performance. ShelfGaussian is further evaluated on an unmanned ground vehicle (UGV) to assess its in the-wild performance across diverse urban scenarios. Project website: https://lunarlab-gatech.github.io/ShelfGaussian/.

cs.CV↗

arXiv2025

Towards Tighter Convex Relaxation of Mixed-Integer Programs: Leveraging Logic Network Flow for Task and Motion Planning

This paper proposes an optimization-based task and motion planning framework, named "Logic Network Flow," that integrates temporal logic specifications into mixed-integer programs for efficient robot planning. Inspired by the Graph-of-Convex-Sets formulation, temporal predicates are encoded as polyhedral constraints on each edge of a network flow model, instead of as constraints between nodes in traditional Logic Tree formulations. We further propose a network-flow-based Fourier-Motzkin elimination procedure that removes continuous flow variables while preserving convex relaxation tightness, leading to provably tighter convex relaxations and fewer constraints than Logic Tree formulations. For temporal logic motion planning with piecewise-affine dynamical systems, comprehensive experiments across vehicle routing, multi-robot coordination, and temporal logic control on dynamical systems using point-mass and linear inverted pendulum models demonstrate computational speedups of up to several orders of magnitude, alongside reduced memory consumption. Hardware demonstrations with quadrupedal robots validate real-time replanning capabilities under dynamically changing environmental conditions. The project website is at https://logicnetworkflow.github.io/.

cs.RO↗

arXiv2020

New Security Challenges on Machine Learning Inference Engine: Chip Cloning and Model Reverse Engineering

Machine learning inference engine is of great interest to smart edge computing. Compute-in-memory (CIM) architecture has shown significant improvements in throughput and energy efficiency for hardware acceleration. Emerging non-volatile memory technologies offer great potential for instant on and off by dynamic power gating. Inference engine is typically pre-trained by the cloud and then being deployed to the filed. There are new attack models on chip cloning and neural network model reverse engineering. In this paper, we propose countermeasures to the weight cloning and input-output pair attacks. The first strategy is the weight fine-tune to compensate the analog-to-digital converter (ADC) offset for a specific chip instance while inducing significant accuracy drop for cloned chip instances. The second strategy is the weight shuffle and fake rows insertion to allow accurate propagation of the activations of the neural network only with a key.

eess.SP↗