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Yuming Liu

6 accepted papers

2025

Configuration-Adaptive Visual Relative Localization for Spherical Modular Self-Reconfigurable Robots

ICRA 2025

Spherical Modular Self-reconfigurable Robots (SMSRs) have been popular in recent years. Their Self-reconfigurable nature allows them to adapt to different en-vironments and tasks, and achieve what a single module could not achieve. To collaborate with each other, relative localization between each m

Cited by 0SourceScholar
2025

Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting

AAAI 2025technical

Current methods for time series forecasting struggle in the online scenario, since it is difficult to preserve long-term dependency while adapting short-term changes when data are arriving sequentially. Although some recent methods solve this problem by controlling the updates of latent states, they…

2025

Transferring Visual Knowledge: Semi-Supervised Instance Segmentation for Object Navigation Across Varying Height Viewpoints

ICRA 2025

The object navigation task requires robots to understand the semantic regularities in their environments. However, existing modular object navigation frameworks rely on instance segmentation models trained at fixed camera height viewpoints, limiting generalization performance and increasing labeling

Cited by 0SourcecodeScholar
2023

Dynamic Obstacle Avoidance for Cable-Driven Parallel Robots With Mobile Bases via Sim-to-Real Reinforcement Learning

RA-L 2023

A Cable-Driven Parallel Robot (CDPR) with Mobile Bases (MBs) can modify its geometric architecture and is suitable for manipulation tasks in constrained environments. In manipulation tasks, a CDPR with MBs inevitably encounters obstacles, including dynamic obstacles. However, the high dimensional st

Cited by 31SourceScholar
2022

HousE: Knowledge Graph Embedding with Householder Parameterization

ICML 2022spotlight

The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can only capture some of them with insufficient modeling capacity. In this work, we propose a more powerful KGE framework name…