28 citations · 68 across the 9 of their papers we have counts for
9 papers · 1 filter
Advances, challenges, and opportunities for legged robots
Jonas Frey, Matías Mattamala, Hae-Won Park +5
Humanoid and quadrupedal robots have the potential to revolutionize the way we work, interact, and coexist with intelligent machines. To understand their effects on society and how…
Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
Jun-Gill Kang, Jaehyun Park, Tae-Gyu Song +3
Enabling quadrupedal robots to traverse complex terrains-from rugged outdoor environments to urban landscapes-requires seamless integration of multiple motor skills, smooth transit…
Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer
Dongyun Kang, Min-Gyu Kim, Tae-Gyu Song +3
Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various legged locomotion tasks, producing…
Multi-Sensor Fusion for Quadruped Robot State Estimation using Invariant Filtering and Smoothing
Ylenia Nisticò, Hajun Kim, João Carlos Virgolino Soares +3
This letter introduces two multi-sensor state estimation frameworks for quadruped robots, built on the Invariant Extended Kalman Filter (InEKF) and Invariant Smoother (IS). The pro…
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…
Online Friction Coefficient Identification for Legged Robots on Slippery Terrain Using Smoothed Contact Gradients
Hajun Kim, Dongyun Kang, Min-Gyu Kim +2
This paper proposes an online friction coefficient identification framework for legged robots on slippery terrain. The approach formulates the optimization problem to minimize the…
Actuator-Constrained Reinforcement Learning for High-Speed Quadrupedal Locomotion
Young-Ha Shin, Tae-Gyu Song, Gwanghyeon Ji +1
This paper presents a method for achieving high-speed running of a quadruped robot by considering the actuator torque-speed operating region in reinforcement learning. The physical…
Contact-Implicit Model Predictive Control: Controlling Diverse Quadruped Motions Without Pre-Planned Contact Modes or Trajectories
Gijeong Kim, Dongyun Kang, Joon-Ha Kim +2
This paper presents a contact-implicit model predictive control (MPC) framework for the real-time discovery of multi-contact motions, without predefined contact mode sequences or f…
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…