Papers by Stylianos Piperakis
International Conference on Intelligent Robots and Systems (IROS), 2019
Contemporary humanoids are equipped with visual and LiDAR sensors that are effectively utilized f... more Contemporary humanoids are equipped with visual and LiDAR sensors that are effectively utilized for Visual Odometry (VO) and LiDAR Odometry (LO). Unfortunately, such measurements commonly suffer from outliers in a dynamic environment, since frequently it is assumed that only the robot is in motion and the world is static. To this end, robust state estimation schemes are mandatory in order for humanoids to symbiotically co-exist with humans in their daily dynamic environments. In this article, the robust Gaussian Error-State Kalman Filter for humanoid robot locomotion is presented. The introduced method automatically detects and rejects outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Subsequently, the proposed method is quantitatively and qualitatively assessed in realistic conditions with the full-size humanoid robot WALK-MAN v2.0 and the mini-size humanoid robot NAO to demonstrate its accuracy and robustness when outlier VO/LO measurements are present. Finally, in order to reinforce further research endeavours, our implementation is released as an open-source ROS/C++ package.
International Conference on Intelligent Robots and Systems (IROS), 2018
This article presents a novel cascade state estimation framework for 3D Center of Mass (CoM) esti... more This article presents a novel cascade state estimation framework for 3D Center of Mass (CoM) estimation of walking humanoid robots. The proposed framework, called SEROW (State Estimation RObot Walking) fuses effectively joint encoder, inertial, feet pressure and visual odometry measurements. Initially, we consider the humanoid's Newton-Euler dynamics and rigorously derive the non-linear CoM estimator. The latter accurately estimates the 3D-CoM position, velocity and external forces acting on the CoM, while directly considering the presence of uneven terrain and the body's angular momentum rate and thus effectively coupling the frontal with the lateral plane dynamics. Furthermore, we extend an established floating mass estimator to take into account the support foot pose, yielding in such a way the mandatory, for CoM estimation, affine transformations and forming a cascade state estimation scheme. Subsequently, we quantitatively and qualitatively assess the proposed scheme by comparing it to other estimation structures in terms of accuracy and robustness to disturbances, both in simulation and on an actual NAO robot walking outdoors over an inclined terrain. To facilitate further research endeavors, our implementation is offered as an open-source ROS/C++ package.
International Conference on Robotics and Automation (ICRA), 2019
Contact detection is an important topic in contemporary humanoid robotic research. Up to date con... more Contact detection is an important topic in contemporary humanoid robotic research. Up to date control and state estimation schemes readily assume that feet contact status is known in advance. In this work, we elaborate on a broader question: in which gait phase is the robot currently in? We introduce an unsupervised learning framework for gait phase estimation based solely on proprioceptive sensing, namely joint encoder, inertial measurement unit and force/torque data. Initially , a meaningful physical explanation on data acquisition is presented. Subsequently, dimensionality reduction is performed to obtain a compact low-dimensional feature representation followed by clustering into three groups, one for each gait phase. The proposed framework is qualitatively and quantitatively assessed in simulation with ground-truth data of uneven/rough terrain walking gaits and insights about the latent gait phase dynamics are drawn. Additionally, its efficacy and robustness is demonstrated when incorporated in leg odometry computation. Since our implementation is based on sensing that is commonly available on humanoids today, we release an open-source ROS/Python package to reinforce further research endeavors.
International Conference on Robotics and Automation 3rd Workshop for Legged Robots, 2019
Visual Simultaneous Localization and Mapping (visual SLAM) constitutes a challenging task when ap... more Visual Simultaneous Localization and Mapping (visual SLAM) constitutes a challenging task when applied to humanoid robots. While walking, the robot's feet strike the ground and generate sudden accelerations due to rapid and sequential contact switching. These in turn give rise to visual motion blurriness that greatly compromises the performance of the system. In this work we present a dense visual SLAM approach that integrates information from IMU, robot kinematics and contact measurements, to overcome these issues.
—This letter presents a novel cascade state estimation framework for the three-dimensional (3-D) ... more —This letter presents a novel cascade state estimation framework for the three-dimensional (3-D) center of mass (CoM) estimation of walking humanoid robots. The proposed framework, called State Estimation RObot Walking (SEROW), fuses effectively joint encoder, inertial, feet pressure, and visual odometry measurements. Initially, we consider the humanoid's Newton–Euler dynamics and rigorously derive the nonlinear CoM estimator. The latter accurately estimates the 3D-CoM position, velocity, and external forces acting on the CoM, while directly considering the presence of uneven terrain and the body's angular momentum rate and, thus, effectively coupling the frontal with the lateral plane dynamics. Furthermore, we extend an established floating mass estimator to take into account the support foot pose, yielding in such a way the mandatory, for CoM estimation, affine transformations and forming a cascade state estimation scheme. Subsequently, we quantitatively and qualitatively assess the proposed scheme by comparing it to other estimation structures in terms of accuracy and robustness to disturbances, both in simulation and on an actual NAO robot walking outdoors over an inclined terrain. To facilitate further research endeavors, our implementation is offered as an open-source ROS/C++ package.
—Learning from Demonstration (LfD) is addressed in this work in order to establish a novel framew... more —Learning from Demonstration (LfD) is addressed in this work in order to establish a novel framework for Human-Robot Collaborative (HRC) task execution. In this context, a robotic system is trained to perform various actions by observing a human demonstrator. We formulate a latent representation of observed behaviors and associate this representation with the corresponding one for target robotic behaviors. Effectively, a mapping of observed to performed actions is defined, that abstracts action variations and differences between the human and robotic manipulators, and facilitates execution of newly-observed actions. The learned action-behaviors are then employed to accomplish task execution in an HRC scenario. Experimental results obtained regard the successful training of a robotic arm with various action behaviors and its subsequent deployment in HRC task accomplishment. The latter demonstrate the validity and efficacy of the proposed approach in human-robot collabo-rative setups.
Predictive control for dynamic locomotion of real humanoid robots
— This article presents a complete formulation of the challenging task of stable humanoid robot o... more — This article presents a complete formulation of the challenging task of stable humanoid robot omnidirectional walk based on the Cart and Table model for approximating the robot dynamics. For the control task, we propose two novel approaches: preview control augmented with the inverse system for negotiating strong disturbances and uneven terrain and linear model-predictive control approximated by an orthonormal basis for computational efficiency coupled with constraints for stability. For the generation of smooth feet trajectories, we present a new approach based on rigid body interpolation, enhanced by adaptive step correction. Finally, we present a sensor fusion approach for sensor-based state estimation and an effective solution to sensors' noise, delay, and bias issues, as well as to errors induced by the simplified dynamics and actuation imperfections. Our formulation is applied on a real NAO humanoid robot, where it achieves real-time onboard execution and yields smooth and stable gaits.
— This article presents a novel state estimation scheme for humanoid robot locomotion using an Ex... more — This article presents a novel state estimation scheme for humanoid robot locomotion using an Extended Kalman Filter (EKF) for fusing encoder, inertial and Foot Sensitive Resistor (FSR) measurements. The filter's model is based on the non-linear Zero Moment Point (ZMP) dynamics and thus, coupling the dynamic behavior in the frontal and the lateral plane. Furthermore, it provides state estimates for variables that are commonly used by walking pattern generators and posture balance controllers, such as the Center of Mass (CoM) and the linear time-varying Divergent Component of Motion (DCM) position and velocity, in the 3-D space. Modeling errors are taken into account as external forces acting on the robot in the acceleration level. In addition, an observability analysis for the non-linear system dynamics and the linearized discrete-time EKF dynamics is presented. Subsequently, by utilizing ground-truth data obtained from a vicon motion capture system with a NAO humanoid robot, we demonstrate the effectiveness and robustness of the proposed scheme contrasted to the linear filters, even in the case where disturbances are introduced to the system. Finally, the proposed approach is implemented and employed for feedback to a real-time posture controller, rendering a NAO robot able to walk on an outdoors inclined pavement.
Thesis Chapters by Stylianos Piperakis
Center of Mass (CoM) estimation realizes a crucial role in legged locomotion. Most walking patter... more Center of Mass (CoM) estimation realizes a crucial role in legged locomotion. Most walking pattern generators and real-time gait stabilizers commonly assume that the CoM position and velocity are available for feedback. In this thesis we present one of the first
3D-CoM state estimators for humanoid robot walking. The proposed estimation scheme fuses effectively joint encoder, inertial, and feet pressure measurements with an Extended Kalman Filter (EKF) to accurately estimate the 3D-CoM position, velocity, and external
forces acting on the CoM. Furthermore, it directly considers the presence of uneven terrain and the body’s angular momentum rate and thus effectively couples the frontal with the lateral plane dynamics, without relying on feet Force/Torque (F/T) sensing.
Nevertheless, it is common practice to transform the measurements to a world frame of reference and estimate the CoM with respect to the world frame. Consequently, the robot’s base and support foot pose are mandatory and need to be co-estimated. To this end, we extend a well-established in literature floating mass estimator to account for the
support foot dynamics and fuse kinematic-inertial measurements with the Error State Kalman Filter (ESKF) to appropriately handle the overparametrization of rotations. In such a way, a cascade state estimation scheme consisting of a base and a CoM estimator
is formed and coined State Estimation RObot Walking (SEROW). Additionally, we employ Visual Odometry (VO) and/or LIDAR Odometry (LO) measurements to correct the kinematic drift caused by slippage during walking. Unfortunately, such measurements suffer from outliers in a dynamic environment, since frequently it is assumed that only the
robot is in motion and the world around is static. Thus, we introduce the Robust Gaussian ESKF (RGESKF) to automatically detect and reject outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Therefore, SEROW
is robustified and is suitable for dynamic human environments. In order to reinforce further research endeavors, SEROW is released to the robotic community as an open-source ROS/C++ package.
Up to date control and state estimation schemes readily assume that feet contact status is known a priori. Contact detection is an important and largely unexplored topic in contemporary humanoid robotics research. In this thesis, we elaborate on a broader question: in which gait phase is the robot currently in? To this end, we propose a holistic frame-
work based on unsupervised learning from proprioceptive sensing that accurately and efficiently addresses this problem. More specifically, we robustly detect one of the three gait-phases, namely Left Single Support (LSS), Double Support (DS), and Right Single Support (RSS) utilizing joint encoder, IMU, and F/T measurements. Initially, dimensionality reduction with Principal Components Analysis (PCA) or autoencoders is performed to extract useful features, obtain a compact representation, and reduce the noise. Next, clustering is performed on the low-dimensional latent space with Gaussian Mixture Models (GMMs) and three dense clusters corresponding to the gait-phases are obtained. Interestingly, it is
demonstrated that the gait phase dynamics are low-dimensional which is another indication pointing towards locomotion being a low dimensional skill. Accordingly, given that the proposed framework utilizes measurements from sensors that are commonly available
on humanoids nowadays, we offer the Gait-phase Estimation Module (GEM), an open-source ROS/Python implementation to the robotic community.
SEROW and GEM have been quantitatively and qualitatively assessed in terms of accuracy and efficiency both in simulation and under real-world conditions. Initially, a simulated robot in MATLAB and NASA’s Valkyrie humanoid robot in ROS/Gazebo were employed to establish the proposed schemes with uneven/rough terrain gaits. Subsequently,
the proposed schemes were integrated on a) the small size NAO humanoid robot v4.0 and b) the adult size WALK-MAN v2.0 for experimental validation. With NAO, SEROW was implemented on the robot to provide the necessary feedback for motion planning and real-
time gait stabilization to achieve omni-directional locomotion even on outdoor/uneven terrains. Additionally, SEROW was used in footstep planning and also in Visual SLAM with the same robot. Regarding WALK-MAN v2.0, SEROW was executed onboard with kinematic-inertial and F/T data to provide base and CoM feedback in real-time. Furthermore, VO has also been considered to correct the kinematic drift while walking and facilitate possible footstep planning. GEM was also employed to estimate the gait phase in WALK-MAN’s dynamic gaits.
Summarizing, a robust nonlinear state estimator is proposed for humanoid robot walking. Nevertheless, this scheme can be readily extended to other type of legged robots such
as quadrupeds, since they share the same fundamental principles.
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Papers by Stylianos Piperakis
Thesis Chapters by Stylianos Piperakis
3D-CoM state estimators for humanoid robot walking. The proposed estimation scheme fuses effectively joint encoder, inertial, and feet pressure measurements with an Extended Kalman Filter (EKF) to accurately estimate the 3D-CoM position, velocity, and external
forces acting on the CoM. Furthermore, it directly considers the presence of uneven terrain and the body’s angular momentum rate and thus effectively couples the frontal with the lateral plane dynamics, without relying on feet Force/Torque (F/T) sensing.
Nevertheless, it is common practice to transform the measurements to a world frame of reference and estimate the CoM with respect to the world frame. Consequently, the robot’s base and support foot pose are mandatory and need to be co-estimated. To this end, we extend a well-established in literature floating mass estimator to account for the
support foot dynamics and fuse kinematic-inertial measurements with the Error State Kalman Filter (ESKF) to appropriately handle the overparametrization of rotations. In such a way, a cascade state estimation scheme consisting of a base and a CoM estimator
is formed and coined State Estimation RObot Walking (SEROW). Additionally, we employ Visual Odometry (VO) and/or LIDAR Odometry (LO) measurements to correct the kinematic drift caused by slippage during walking. Unfortunately, such measurements suffer from outliers in a dynamic environment, since frequently it is assumed that only the
robot is in motion and the world around is static. Thus, we introduce the Robust Gaussian ESKF (RGESKF) to automatically detect and reject outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Therefore, SEROW
is robustified and is suitable for dynamic human environments. In order to reinforce further research endeavors, SEROW is released to the robotic community as an open-source ROS/C++ package.
Up to date control and state estimation schemes readily assume that feet contact status is known a priori. Contact detection is an important and largely unexplored topic in contemporary humanoid robotics research. In this thesis, we elaborate on a broader question: in which gait phase is the robot currently in? To this end, we propose a holistic frame-
work based on unsupervised learning from proprioceptive sensing that accurately and efficiently addresses this problem. More specifically, we robustly detect one of the three gait-phases, namely Left Single Support (LSS), Double Support (DS), and Right Single Support (RSS) utilizing joint encoder, IMU, and F/T measurements. Initially, dimensionality reduction with Principal Components Analysis (PCA) or autoencoders is performed to extract useful features, obtain a compact representation, and reduce the noise. Next, clustering is performed on the low-dimensional latent space with Gaussian Mixture Models (GMMs) and three dense clusters corresponding to the gait-phases are obtained. Interestingly, it is
demonstrated that the gait phase dynamics are low-dimensional which is another indication pointing towards locomotion being a low dimensional skill. Accordingly, given that the proposed framework utilizes measurements from sensors that are commonly available
on humanoids nowadays, we offer the Gait-phase Estimation Module (GEM), an open-source ROS/Python implementation to the robotic community.
SEROW and GEM have been quantitatively and qualitatively assessed in terms of accuracy and efficiency both in simulation and under real-world conditions. Initially, a simulated robot in MATLAB and NASA’s Valkyrie humanoid robot in ROS/Gazebo were employed to establish the proposed schemes with uneven/rough terrain gaits. Subsequently,
the proposed schemes were integrated on a) the small size NAO humanoid robot v4.0 and b) the adult size WALK-MAN v2.0 for experimental validation. With NAO, SEROW was implemented on the robot to provide the necessary feedback for motion planning and real-
time gait stabilization to achieve omni-directional locomotion even on outdoor/uneven terrains. Additionally, SEROW was used in footstep planning and also in Visual SLAM with the same robot. Regarding WALK-MAN v2.0, SEROW was executed onboard with kinematic-inertial and F/T data to provide base and CoM feedback in real-time. Furthermore, VO has also been considered to correct the kinematic drift while walking and facilitate possible footstep planning. GEM was also employed to estimate the gait phase in WALK-MAN’s dynamic gaits.
Summarizing, a robust nonlinear state estimator is proposed for humanoid robot walking. Nevertheless, this scheme can be readily extended to other type of legged robots such
as quadrupeds, since they share the same fundamental principles.
3D-CoM state estimators for humanoid robot walking. The proposed estimation scheme fuses effectively joint encoder, inertial, and feet pressure measurements with an Extended Kalman Filter (EKF) to accurately estimate the 3D-CoM position, velocity, and external
forces acting on the CoM. Furthermore, it directly considers the presence of uneven terrain and the body’s angular momentum rate and thus effectively couples the frontal with the lateral plane dynamics, without relying on feet Force/Torque (F/T) sensing.
Nevertheless, it is common practice to transform the measurements to a world frame of reference and estimate the CoM with respect to the world frame. Consequently, the robot’s base and support foot pose are mandatory and need to be co-estimated. To this end, we extend a well-established in literature floating mass estimator to account for the
support foot dynamics and fuse kinematic-inertial measurements with the Error State Kalman Filter (ESKF) to appropriately handle the overparametrization of rotations. In such a way, a cascade state estimation scheme consisting of a base and a CoM estimator
is formed and coined State Estimation RObot Walking (SEROW). Additionally, we employ Visual Odometry (VO) and/or LIDAR Odometry (LO) measurements to correct the kinematic drift caused by slippage during walking. Unfortunately, such measurements suffer from outliers in a dynamic environment, since frequently it is assumed that only the
robot is in motion and the world around is static. Thus, we introduce the Robust Gaussian ESKF (RGESKF) to automatically detect and reject outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Therefore, SEROW
is robustified and is suitable for dynamic human environments. In order to reinforce further research endeavors, SEROW is released to the robotic community as an open-source ROS/C++ package.
Up to date control and state estimation schemes readily assume that feet contact status is known a priori. Contact detection is an important and largely unexplored topic in contemporary humanoid robotics research. In this thesis, we elaborate on a broader question: in which gait phase is the robot currently in? To this end, we propose a holistic frame-
work based on unsupervised learning from proprioceptive sensing that accurately and efficiently addresses this problem. More specifically, we robustly detect one of the three gait-phases, namely Left Single Support (LSS), Double Support (DS), and Right Single Support (RSS) utilizing joint encoder, IMU, and F/T measurements. Initially, dimensionality reduction with Principal Components Analysis (PCA) or autoencoders is performed to extract useful features, obtain a compact representation, and reduce the noise. Next, clustering is performed on the low-dimensional latent space with Gaussian Mixture Models (GMMs) and three dense clusters corresponding to the gait-phases are obtained. Interestingly, it is
demonstrated that the gait phase dynamics are low-dimensional which is another indication pointing towards locomotion being a low dimensional skill. Accordingly, given that the proposed framework utilizes measurements from sensors that are commonly available
on humanoids nowadays, we offer the Gait-phase Estimation Module (GEM), an open-source ROS/Python implementation to the robotic community.
SEROW and GEM have been quantitatively and qualitatively assessed in terms of accuracy and efficiency both in simulation and under real-world conditions. Initially, a simulated robot in MATLAB and NASA’s Valkyrie humanoid robot in ROS/Gazebo were employed to establish the proposed schemes with uneven/rough terrain gaits. Subsequently,
the proposed schemes were integrated on a) the small size NAO humanoid robot v4.0 and b) the adult size WALK-MAN v2.0 for experimental validation. With NAO, SEROW was implemented on the robot to provide the necessary feedback for motion planning and real-
time gait stabilization to achieve omni-directional locomotion even on outdoor/uneven terrains. Additionally, SEROW was used in footstep planning and also in Visual SLAM with the same robot. Regarding WALK-MAN v2.0, SEROW was executed onboard with kinematic-inertial and F/T data to provide base and CoM feedback in real-time. Furthermore, VO has also been considered to correct the kinematic drift while walking and facilitate possible footstep planning. GEM was also employed to estimate the gait phase in WALK-MAN’s dynamic gaits.
Summarizing, a robust nonlinear state estimator is proposed for humanoid robot walking. Nevertheless, this scheme can be readily extended to other type of legged robots such
as quadrupeds, since they share the same fundamental principles.