1 citations · 2 across the 7 of their papers we have counts for
7 papers
Bicycle Acrobatics with Reinforcement Learning
Shamel Fahmi, Arianna Ilvonen, Samuel Zapolsky +8
Bicycle robots are fast and energy efficient, but their simple mechanical design and their underactuated and non-holonomic dynamics make highly agile maneuvers difficult to achieve…
System Design of the Ultra Mobility Vehicle: A Driving, Balancing, and Jumping Bicycle Robot
Benjamin Bokser, Daniel Gonzalez, Aaron Preston +39
Trials cyclists and mountain bike riders can hop, jump, balance, and drive on one or both wheels. This versatility allows them to achieve speed and energy-efficiency on smooth terr…
Reinforcement Learning for Reduced-order Models of Legged Robots
Yu-Ming Chen, Hien Bui, Michael Posa
Model-based approaches for planning and control for bipedal locomotion have a long history of success. It can provide stability and safety guarantees while being effective in accom…
Beyond Inverted Pendulums: Task-optimal Simple Models of Legged Locomotion
Yu-Ming Chen, Jianshu Hu, Michael Posa
Reduced-order models (ROM) are popular in online motion planning due to their simplicity. A good ROM for control captures critical task-relevant aspects of the full dynamics while…
Integrable Whole-body Orientation Coordinates for Legged Robots
Yu-Ming Chen, Gabriel Nelson, Robert Griffin +2
Complex multibody legged robots can have complex rotational control challenges. In this paper, we propose a concise way to understand and formulate a \emph{whole-body orientation}…
Optimal Reduced-order Modeling of Bipedal Locomotion
Yu-Ming Chen, Michael Posa
State-of-the-art approaches to legged locomotion are widely dependent on the use of models like the linear inverted pendulum (LIP) and the spring-loaded inverted pendulum (SLIP), p…
Learning Stable and Energetically Economical Walking with RAMone
Audrow Nash, Yu-Ming Chen, Nils Smit-Anseeuw +2
In this paper, we optimize over the control parameter space of our planar-bipedal robot, RAMone, for stable and energetically economical walking at various speeds. We formulate thi…