22 citations · 25 across the 6 of their papers we have counts for
6 papers · 1 filter
PPF: Pre-training and Preservative Fine-tuning of Humanoid Locomotion via Model-Assumption-based Regularization
Hyunyoung Jung, Zhaoyuan Gu, Ye Zhao +2
Humanoid locomotion is a challenging task due to its inherent complexity and high-dimensional dynamics, as well as the need to adapt to diverse and unpredictable environments. In t…
Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation
Fukang Liu, Zhaoyuan Gu, Yilin Cai +8
Humanoid robots are designed to perform diverse loco-manipulation tasks. However, they face challenges due to their high-dimensional and unstable dynamics, as well as the complex c…
Safe Navigation of Bipedal Robots via Koopman Operator-Based Model Predictive Control
Jeonghwan Kim, Yunhai Han, Harish Ravichandar +1
Nonlinearity in dynamics has long been a major challenge in robotics, often causing significant performance degradation in existing control algorithms. For example, the navigation…
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…
Words into Action: Learning Diverse Humanoid Robot Behaviors using Language Guided Iterative Motion Refinement
K. Niranjan Kumar, Irfan Essa, Sehoon Ha
Humanoid robots are well suited for human habitats due to their morphological similarity, but developing controllers for them is a challenging task that involves multiple sub-probl…
CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning
Tianyu Li, Hyunyoung Jung, Matthew Gombolay +2
Human motion driven control (HMDC) is an effective approach for generating natural and compelling robot motions while preserving high-level semantics. However, establishing the cor…