Learning to stabilize the head of a quadrupedal robot with an artificial vestibular system

Marcinkiewicz, Marek; Kaushik, Ravi; Labutov, Igor; Parsons, Simon; Raphan, Theodore

doi:10.1109/robot.2009.5152685

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…The effect of head and trunk stabilization was also studied on passive walkers demostrating that the upper-body stabilization regulates naturally the walking limit cycle (Benallegue et al 2013). In robotics literature, there are few implementations of head stabilization models (Yamada et al 2007;Marcinkiewicz et al 2009;Santos et al 2009;Oliveira et al 2011). Yamada et al (2007) propose a method for the stabilization of the snake-like robot head based on the neck control.…”

Section: Head Stabilization In Robotsmentioning

confidence: 99%

“…The results on a Sony AIBO robotic platform show that the head movement is not totally eliminated during locomotion. Another controller (Marcinkiewicz et al 2009) for the head stabilization has been implemented on the Sony AIBO robot. This controller is based on a machine learning algorithm able to learn the compensation for the head movements when no stabilization mechanism is present.…”

Section: Head Stabilization In Robotsmentioning

confidence: 99%

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Head stabilization in a humanoid robot: models and implementations

et al. 2016

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Neuroscientific studies show that humans tend to stabilize their head orientation, while accomplishing a locomotor task. This is beneficial to image stabilization and in general to keep a reference frame for the body. In robotics, too, head stabilization during robot walking provides advantages in robot vision and gaze-guided locomotion. In order to obtain the head movement behaviors found in human walk, it is necessary and sufficient to be able to control the orientation (roll, pitch and yaw) of the head in space. Based on these principles, three controllers have been designed. We developed two classic robotic controllers, an inverse kinematics based controller, an inverse kinematics differential controller and a bio-inspired adaptive controller based on feedback error learning. The controllers use the inertial feedback from a IMU sensor and control neck joints in order to align the head orientation with the global orientation reference. We present the results for the head stabilization controllers, on two sets of experiments, validating the robustness of the proposed control methods. In particular, we focus our analysis on the effectiveness of the bio-inspired adaptive controller against the classic robotic controllers. The first set of experiments, tested on a simulated robot, focused on the controllers response to a set of disturbance frequencies and a step function. The other set of experiments were carried out on the SABIAN robot, where these controllers were implemented in conjunction with a model of the vestibulo-ocular reflex (VOR) and opto-kinetic reflex (OKR). Such a setup permits to compare the performances of the considered head stabilization controllers in conditions which mimic the human stabilization mechanisms composed of the joint effect of VOR, OKR and stabilization of the head. The results show that the bio-inspired adaptive controller is more beneficial for the stabilization of the head in tasks involving a sinusoidal torso disturbance, and it shows comparable performances to the inverse kinematics controller in case of the step response and the locomotion experiments conducted on the real robot

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Section: Head Stabilization In Robotsmentioning

confidence: 99%

Section: Head Stabilization In Robotsmentioning

confidence: 99%

Head stabilization in a humanoid robot: models and implementations

et al. 2016

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“…This finding makes it possible to create surface classifiers by collecting training data with short runs of the robot on a given surface and the entire model building process can be reduced considerably. [13] or collected bigger sample database to overcome its noisy accelerometer [21], this paper implemented sensor fusion to get more reliable surface recognition with the built-in sensors. Several classifiers were examined and naive Bayes was found the best for building terrain models.…”

Section: Performance Evaluation With Small Training Setmentioning

confidence: 99%

“…The popular Kalman filter was applied to a MEMS sensor by Qin et al [15] to detect the posture of a wheeled robot, while in a particular research [13], an extra Inertia Measurement Unit (IMU) device was mounted on AIBO to stabilize the head during locomotion. In [9], the directional bias was integrated from the x and y dimensions of an accelerometer.…”

Section: Introductionmentioning

confidence: 99%

Exploring surface detection for a quadruped robot in households

Kertész¹

2014

2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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Surface recognition is essential for legged robots because they need to maintain their dynamic balance on a regular or uneven terrain. The accelerometer is a widely-used tool for this purpose, but the quadruped Sony AIBO does not have such a high-end sensor compared to the latest state-of-art developments. Past works focused on attaching replacement sensors to the robot dog as well as collecting many samples for machine learning methods although some studies did not address this issue at all. This paper focuses on improvements with sensor fusion of built-in sensors to recognize wider variety of surfaces and get similar or better accuracy than earlier experiments. The combined features are based on the accelerometer and paw sensors to make the recognition more robust and a naive Bayes classifier achieves 85-91% accuracy for different locomotion speeds. Evaluation suggests that this method can reduce the data collection time for training samples dramatically and it is suitable for practical applications.

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“…The only mechanical motion stabilization developed on a quadruped has been shown in refs. [17] and [18], where the authors have equipped Sony's 1.5 kg robot AIBO with a system that mimics the inertial sensing mechanisms of the vestibular system of mammals. Their approach can learn how to compensate for the stabilization of head movements.…”

Section: Digital Image Stabilizationmentioning

confidence: 99%

Active camera stabilization to enhance the vision of agile legged robots

Bazeille

Ortiz

Rovida³

et al. 2015

Robotica

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SUMMARYLegged robots have the potential to navigate in more challenging terrains than wheeled robots. Unfortunately, their control is more demanding, because they have to deal with the common tasks of mapping and path planning as well as more specific issues of legged locomotion, like balancing and foothold planning. In this paper, we present the integration and the development of a stabilized vision system on the fully torque-controlled hydraulically actuated quadruped robot (HyQ). The active head added onto the robot is composed of a fast pan and tilt unit (PTU) and a high-resolution wide angle stereo camera. The PTU enables camera gaze shifting to a specific area in the environment (both to extend and refine the map) or to track an object while navigating. Moreover, as the quadruped locomotion induces strong regular vibrations, impacts or slippages on rough terrain, we took advantage of the PTU to mechanically compensate for the robot's motions. In this paper, we demonstrate the influence of legged locomotion on the quality of the visual data stream by providing a detailed study of HyQ's motions, which are compared against a rough terrain wheeled robot of the same size. Our proposed Inertial Measurement Unit (IMU)-based controller allows us to decouple the camera from the robot motions. We show through experiments that, by stabilizing the image feedback, we can improve the onboard vision-based processes of tracking and mapping. In particular, during the outdoor tests on the quadruped robot, the use of our camera stabilization system improved the accuracy on the 3D maps by 25%, with a decrease of 50% of mapping failures.

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Learning to stabilize the head of a quadrupedal robot with an artificial vestibular system

Cited by 11 publications

References 14 publications

Head stabilization in a humanoid robot: models and implementations

Head stabilization in a humanoid robot: models and implementations

Exploring surface detection for a quadruped robot in households

Active camera stabilization to enhance the vision of agile legged robots

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