57 research outputs found
Agile and Versatile Robot Locomotion via Kernel-based Residual Learning
This work developed a kernel-based residual learning framework for
quadrupedal robotic locomotion. Initially, a kernel neural network is trained
with data collected from an MPC controller. Alongside a frozen kernel network,
a residual controller network is trained via reinforcement learning to acquire
generalized locomotion skills and resilience against external perturbations.
With this proposed framework, a robust quadrupedal locomotion controller is
learned with high sample efficiency and controllability, providing
omnidirectional locomotion at continuous velocities. Its versatility and
robustness are validated on unseen terrains that the expert MPC controller
fails to traverse. Furthermore, the learned kernel can produce a range of
functional locomotion behaviors and can generalize to unseen gaits
Verifying Safe Transitions between Dynamic Motion Primitives on Legged Robots
Functional autonomous systems often realize complex tasks by utilizing state
machines comprised of discrete primitive behaviors and transitions between
these behaviors. This architecture has been widely studied in the context of
quasi-static and dynamics-independent systems. However, applications of this
concept to dynamical systems are relatively sparse, despite extensive research
on individual dynamic primitive behaviors, which we refer to as "motion
primitives." This paper formalizes a process to determine dynamic-state aware
conditions for transitions between motion primitives in the context of safety.
The result is framed as a "motion primitive graph" that can be traversed by
standard graph search and planning algorithms to realize functional autonomy.
To demonstrate this framework, dynamic motion primitives -- including standing
up, walking, and jumping -- and the transitions between these behaviors are
experimentally realized on a quadrupedal robot
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
RSG: Fast Learning Adaptive Skills for Quadruped Robots by Skill Graph
Developing robotic intelligent systems that can adapt quickly to unseen wild
situations is one of the critical challenges in pursuing autonomous robotics.
Although some impressive progress has been made in walking stability and skill
learning in the field of legged robots, their ability to fast adaptation is
still inferior to that of animals in nature. Animals are born with massive
skills needed to survive, and can quickly acquire new ones, by composing
fundamental skills with limited experience. Inspired by this, we propose a
novel framework, named Robot Skill Graph (RSG) for organizing massive
fundamental skills of robots and dexterously reusing them for fast adaptation.
Bearing a structure similar to the Knowledge Graph (KG), RSG is composed of
massive dynamic behavioral skills instead of static knowledge in KG and enables
discovering implicit relations that exist in be-tween of learning context and
acquired skills of robots, serving as a starting point for understanding subtle
patterns existing in robots' skill learning. Extensive experimental results
demonstrate that RSG can provide rational skill inference upon new tasks and
environments and enable quadruped robots to adapt to new scenarios and learn
new skills rapidly
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