6 papers · 1 filter
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…
Reinforcement Learning-based Robust Wall Climbing Locomotion Controller in Ferromagnetic Environment
Yong Um, Young-Ha Shin, Joon-Ha Kim +2
We present a reinforcement learning framework for quadrupedal wall-climbing locomotion that explicitly addresses uncertainty in magnetic foot adhesion. A physics-based adhesion mod…
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…
Real time A* Adaptive Action Set Footstep Planning with Human Locomotion Energy Approximations Considering Angle Difference for Heuristic Function
Joon-Ha Kim
The problem of navigating a bipedal robot to a desired destination in various environments is very important. However, it is very difficult to solve the navigation problem in real…
Monte Carlo Tree Search Gait Planner for Non-Gaited Legged System Control
Lorenzo Amatucci, Joon-Ha Kim, Jemin Hwangbo +1
In this work, a non-gaited framework for legged system locomotion is presented. The approach decouples the gait sequence optimization by considering the problem as a decision-makin…
STEP: State Estimator for Legged Robots Using a Preintegrated foot Velocity Factor
Yeeun Kim, Byeongho Yu, Eungchang Mason Lee +3
We propose a novel state estimator for legged robots, STEP, achieved through a novel preintegrated foot velocity factor. In the preintegrated foot velocity factor, the usual non-sl…