22 citations · 32 across the 6 of their papers we have counts for
7 papers
PolyMorph: Learning Locomotion Policy for Cross-Embodiment Adaptation
Hyunsik Oh, Seunghun Jeon, Donghoon Youm +5
Reinforcement learning has enabled robust locomotion for legged robots, but learned policies are typically tied to a specific robot morphology and require additional engineering wh…
Learning Dynamic Pick-and-Place for a Legged Manipulator
Moonkyu Jung, Jiseong Lee, Zhengmao He +8
Legged manipulators extend robotic capabilities beyond static manipulation by integrating agile locomotion with versatile arm control. However, achieving precise manipulation while…
High-speed control and navigation for quadrupedal robots on complex and discrete terrain
Hyeongjun Kim, Hyunsik Oh, Jeongsoo Park +5
High-speed legged navigation in discrete and geometrically complex environments is a challenging task because of the high-degree-of-freedom dynamics and long-horizon, nonconvex nat…
Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy
Yunho Kim, Jeong Hyun Lee, Choongin Lee +4
For reliable autonomous robot navigation in urban settings, the robot must have the ability to identify semantically traversable terrains in the image based on the semantic underst…
Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network
Donghoon Youm, Hyunsik Oh, Suyoung Choi +2
This paper introduces a novel proprioceptive state estimator for legged robots that combines model-based filters and deep neural networks. Recent studies have shown that neural net…
Imitating and Finetuning Model Predictive Control for Robust and Symmetric Quadrupedal Locomotion
Donghoon Youm, Hyunyoung Jung, Hyeongjun Kim +3
Control of legged robots is a challenging problem that has been investigated by different approaches, such as model-based control and learning algorithms. This work proposes a nove…
Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion
Yunho Kim, Hyunsik Oh, Jeonghyun Lee +5
Several earlier studies have shown impressive control performance in complex robotic systems by designing the controller using a neural network and training it with model-free rein…