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Ya Wu

7 accepted papers

2026

GOOD: Geometry-guided Out-of-Distribution Modeling for Open-set Test-time Adaptation in Point Cloud Semantic Segmentation

ICLR 2026poster

Open-set Test-time Adaptation (OSTTA) has been introduced to address the challenges of both online model optimization and open-set recognition. Despite the demonstrated success of OSTTA methodologies in 2D image recognition, their application to 3D point cloud semantic segmentation is still hindered…

Cited by 0SourceScholar
2026

MGS-Track: Monocular 6DoF Pose Tracking Via Masked 3D Prior and Online Gaussian Splatting

ICRA 2026poster

Tracking the 6DoF pose of previously unseen objects from monocular RGB videos is crucial for robotic manipulation, yet remains challenging due to depth ambiguity and limited object-centric visual context. Existing trackers often rely on accurate depth sensors, which constrains deployment in low-cost…

Cited by 0Scholar
2025

CHPO: Constrained Hybrid-action Policy Optimization for Reinforcement Learning

NeurIPS 2025poster

Constrained hybrid-action reinforcement learning (RL) promises to learn a safe policy within a parameterized action space, which is particularly valuable for safety-critical applications involving discrete-continuous hybrid action spaces. However, existing hybrid-action RL algorithms primarily focus…

Cited by 0SourceScholar
2025

Multimodal LiDAR-Camera Novel View Synthesis with Unified Pose-free Neural Fields

NeurIPS 2025poster

Pose-free Neural Radiance Field (NeRF) aims at novel view synthesis (NVS) without relying on accurate poses, exhibiting significant practical value. Image and LiDAR point cloud are two pivotal modalities in autonomous driving scenarios. While demonstrating impressive performance, single-modality pos…

Cited by 0SourceScholar
2025

OOD-Barrier: Build a Middle-Barrier for Open-Set Single-Image Test Time Adaptation via Vision Language Models

NeurIPS 2025poster

In real-world environments, a well-designed model must be capable of handling dynamically evolving distributions, where both in-distribution (ID) and out-of-distribution (OOD) samples appear unpredictably and individually, making real-time adaptation particularly challenging. While open-set test-tim…

Cited by 0SourceScholar
2025

The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas

EMNLP 2025

Ethical decision-making is a critical aspect of human judgment, and the growing use of LLMs in decision-support systems necessitates a rigorous evaluation of their moral reasoning capabilities. However, existing assessments primarily rely on single-step evaluations, failing to capture how models ada

Cited by 0SourcePDFScholar
2023

Continual Pedestrian Trajectory Learning With Social Generative Replay

RA-L 2023

Learning to predict the trajectories of pedestrians is essential for improving safety and efficiency of mobile robots. The prediction is challenging since the robot needs to operate in multiple environments in which the motion patterns of pedestrians are different between environments. Existing pede

Cited by 19SourceScholar