47 citations · 49 across the 7 of their papers we have counts for
7 papers
Feasibility-Guided Planning over Multi-Specialized Locomotion Policies
Ying-Sheng Luo, Lu-Ching Wang, Hanjaya Mandala +6
Planning over unstructured terrain presents a significant challenge in the field of legged robotics. Although recent works in reinforcement learning have yielded various locomotion…
Benchmarking Smoothness and Reducing High-Frequency Oscillations in Continuous Control Policies
Guilherme Christmann, Ying-Sheng Luo, Hanjaya Mandala +1
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, cate…
Expert Composer Policy: Scalable Skill Repertoire for Quadruped Robots
Guilherme Christmann, Ying-Sheng Luo, Wei-Chao Chen
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 sampl…
Expanding Versatility of Agile Locomotion through Policy Transitions Using Latent State Representation
Guilherme Christmann, Ying-Sheng Luo, Jonathan Hans Soeseno +1
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 distributi…
Development of a deep learning-based tool to assist wound classification
Po-Hsuan Huang, Yi-Hsiang Pan, Ying-Sheng Luo +4
This paper presents a deep learning-based wound classification tool that can assist medical personnel in non-wound care specialization to classify five key wound conditions, namely…
Transition Motion Tensor: A Data-Driven Approach for Versatile and Controllable Agents in Physically Simulated Environments
Jonathan Hans Soeseno, Ying-Sheng Luo, Trista Pei-Chun Chen +1
This paper proposes the Transition Motion Tensor, a data-driven framework that creates novel and physically accurate transitions outside of the motion dataset. It enables simulated…
CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion
Ying-Sheng Luo, Jonathan Hans Soeseno, Trista Pei-Chun Chen +1
Motion synthesis in a dynamic environment has been a long-standing problem for character animation. Methods using motion capture data tend to scale poorly in complex environments b…