2 research outputs found
Design a Fall Recovery Strategy for a Wheel-Legged Quadruped Robot Using Stability Feature Space
In this paper, we introduced a conceptual analysis to select stability features when performing predefined and precise motions on robots. By analyzing the different stable poses named features and the possible transitions towards different ones, the introduced concept allows to design more predictable and suitable motions when performing particular tasks. As an example of how the concept can be applied we use it on the fall recovery of the quadruped robot CENTAURO. This robot, which is equipped with a custom hybrid wheel-legged mobility system, have good intrinsic stability as other quadrupeds. However, the characteristics of the rough terrains where it might be deployed require complex maneuvers to cope with possible strong disturbances. To prevent and more importantly recover from falls, realignment of postural responses will not be adequate, and effective recovery procedures should be developed. This paper introduces the details of how the presented conceptual analysis provides and an effective fall recovery routine for CENTAURO based on a state machine. The performance of the proposed approach is evaluated with extensive simulation trials using the dynamic model of the CENTAURO robot showing good effectiveness in recovering the robot after fall on flat and inclined surfaces
Learning dynamic motor skills for terrestrial locomotion
The use of Deep Reinforcement Learning (DRL) has received significantly increased attention
from researchers within the robotics field following the success of AlphaGo, which demonstrated
the superhuman capabilities of deep reinforcement algorithms in terms of solving complex
tasks by beating professional GO players. Since then, an increasing number of researchers
have investigated the potential of using DRL to solve complex high-dimensional robotic tasks,
such as legged locomotion, arm manipulation, and grasping, which are difficult tasks to solve
using conventional optimization approaches.
Understanding and recreating various modes of terrestrial locomotion has been of long-standing interest to roboticists. A large variety of applications, such as rescue missions,
disaster responses and science expeditions, strongly demand mobility and versatility in legged
locomotion to enable task completion. In order to create useful physical robots, it is necessary
to design controllers to synthesize the complex locomotion behaviours observed in humans
and other animals.
In the past, legged locomotion was mainly achieved via analytical engineering approaches.
However, conventional analytical approaches have their limitations, as they require relatively
large amounts of human effort and knowledge. Machine learning approaches, such as DRL,
require less human effort compared to analytical approaches. The project conducted for this
thesis explores the feasibility of using DRL to acquire control policies comparable to, or better
than, those acquired through analytical approaches while requiring less human effort.
In this doctoral thesis, we developed a Multi-Expert Learning Architecture (MELA) that
uses DRL to learn multi-skill control policies capable of synthesizing a diverse set of dynamic
locomotion behaviours for legged robots. We first proposed a novel DRL framework for the
locomotion of humanoid robots. The proposed learning framework is capable of acquiring
robust and dynamic motor skills for humanoids, including balancing, walking, standing-up
fall recovery. We subsequently improved upon the learning framework and design a novel
multi-expert learning architecture that is capable of fusing multiple motor skills together in
a seamless fashion and ultimately deploy this framework on a real quadrupedal robot. The
successful deployment of learned control policies on a real quadrupedal robot demonstrates
the feasibility of using an Artificial Intelligence (AI) based approach for real robot motion control