Ying-Sheng Luo — accepted AI-conference papers

2026

Feasibility-Guided Planning Over Multi-Specialized Locomotion Policies

Ying-Sheng Luo, Lu-Ching Wang, Hanjaya Mandala, Yu-Lun Chou, Guilherme Henrique Galelli Christmann, Yu-Chung Chen +3

ICRA 2026poster

Planning over unstructured terrain presents a significant challenge in the field of legged robotics. Although recent works in reinforcement learning have yielded various locomotion strategies, planning over multiple experts remains a complex issue. Existing approaches encounter several constraints: …

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2024

Benchmarking Smoothness and Reducing High-Frequency Oscillations in Continuous Control Policies

Guilherme Christmann, Ying-Sheng Luo, Hanjaya Mandala, Wei-Chao Chen

IROS 2024poster

Reinforcement learning (RL) policies are prone to high-frequency oscillations, especially undesirable when deploying to hardware in the real-world. In this paper, we identify, categorize, and compare methods from the literature that aim to mitigate high-frequency oscillations in deep RL. We define t…

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2024

Expert Composer Policy: Scalable Skill Repertoire for Quadruped Robots

Guilherme Christmann, Ying-Sheng Luo, Wei-Chao Chen

ICRA 2024poster

We propose the expert composer policy, a framework to reliably expand the skill repertoire of quadruped agents. The composer policy links pair of experts via transitions to a sampled target state, allowing experts to be composed sequentially. Each expert specializes in a single skill, such as a loco…

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2023

Expanding Versatility of Agile Locomotion through Policy Transitions Using Latent State Representation

Guilherme Christmann, Ying-Sheng Luo, Jonathan Hans Soeseno, Wei-Chao Chen

ICRA 2023poster

This paper proposes the transition-net, a robust transition strategy that expands the versatility of robot locomotion in the real-world setting. To this end, we start by distributing the complexity of different gaits into dedicated locomotion policies applicable to real-world robots. Next, we expand…

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