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Chang Shu

7 accepted papers

2026

From Swept Contact to Pose: Probe-Aware Registration Via Complementary-Shape Docking

ICRA 2026poster

Accurate registration between a prior model and the real scene is essential for high-precision robotic manipulation, yet optical methods suffer from long calibration chains, line-of-sight constraints, and fabrication errors. We propose a calibration-free alternative that reformulates contact registr…

2025

All Roads Lead to Rome: Graph-Based Confidence Estimation for Large Language Model Reasoning

EMNLP 2025

Confidence estimation is essential for the reliable deployment of large language models (LLMs). Existing methods are primarily designed for factual QA tasks and often fail to generalize to reasoning tasks. To address this gap, we propose a set of training-free, graph-based confidence estimation meth

Cited by 0SourcePDFScholar
2025

Subassembly to Full Assembly: Effective Assembly Sequence Planning Through Graph-Based Reinforcement Learning

ICRA 2025

This paper proposes an assembly sequence planning framework, named Subassembly to Assembly (S2A). The framework is designed to enable a robotic manipulator to assemble multiple parts in a prespecified structure by leveraging object manipulation actions. The primary technical challenge lies in the ex

Cited by 1SourceScholar
2023

Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark

EMNLP 2023long main

Large language models (LLMs) have been shown to perform well at a variety of syntactic, discourse, and reasoning tasks. While LLMs are increasingly deployed in many forms including conversational agents that interact with humans, we lack a grounded benchmark to measure how well LLMs understand socia…

Cited by 0SourcecodeScholar
2023

POSQA: Probe the World Models of LLMs with Size Comparisons

EMNLP 2023long findings

Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain but also involves interactions with the physical and social environment. With the explosive growth of Large Language Models (LLMs) and their already ubiquitous presence in…

Cited by 0SourcecodeScholar
2020

Feature-metric Loss for Self-supervised Learning of Depth and Egomotion

ECCV 2020poster

Photometric loss is widely used for self-supervised depth and egomotion estimation. However, the loss landscapes induced by photometric differences are often problematic for optimization, caused by plateau landscapes for pixels in texture-less regions or multiple local minima for less discriminative…

2020

Robust Global Optimized Affine Registration Method for Microscopic Images of Biological Tissue

ICASSP 2020accepted

Affine registration can fit the non-rigid deformation of slices effectively, and it is widely used in volume reconstruction of biological tissue. But most of the existing affine registration methods are registered in a given sequence, which results in the accumulation of errors. In this paper, a glo…

Cited by 0SourceScholar