Unsupervised Contact Learning for Humanoid Estimation and Control

This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 201

Trajectory generation for multi-contact momentum-control

Simplified models of the dynamics such as the linear inverted pendulum model (LIPM) have proven to perform well for biped walking on flat ground. However, for more complex tasks the assumptions of these models can become limiting. For example, the LIPM does not allow for the control of contact forces independently, is limited to co-planar contacts and assumes that the angular momentum is zero. In this paper, we propose to use the full momentum equations of a humanoid robot in a trajectory optimization framework to plan its center of mass, linear and angular momentum trajectories. The model also allows for planning desired contact forces for each end-effector in arbitrary contact locations. We extend our previous results on LQR design for momentum control by computing the (linearized) optimal momentum feedback law in a receding horizon fashion. The resulting desired momentum and the associated feedback law are then used in a hierarchical whole body control approach. Simulation experiments show that the approach is computationally fast and is able to generate plans for locomotion on complex terrains while demonstrating good tracking performance for the full humanoid control

Humanoid Momentum Estimation Using Sensed Contact Wrenches

This work presents approaches for the estimation of quantities important for the control of the momentum of a humanoid robot. In contrast to previous approaches which use simplified models such as the Linear Inverted Pendulum Model, we present estimators based on the momentum dynamics of the robot. By using this simple yet dynamically-consistent model, we avoid the issues of using simplified models for estimation. We develop an estimator for the center of mass and full momentum which can be reformulated to estimate center of mass offsets as well as external wrenches applied to the robot. The observability of these estimators is investigated and their performance is evaluated in comparison to previous approaches.Comment: Submitted to the 15th IEEE RAS Humanoids Conference, to be held in Seoul, Korea on November 3 - 5, 201

State Estimation for a Humanoid Robot

This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in [1] on a quadruped platform by incorporating the rotational constraints imposed by the humanoid's flat feet. As in previous work, the proposed Extended Kalman Filter (EKF) accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. The filter employs a sensor-based prediction model which uses inertial data from an IMU and corrects for integrated error using a kinematics-based measurement model which relies on joint encoders and a kinematic model to determine the relative position and orientation of the feet. A nonlinear observability analysis is performed on both the original and updated filters and it is concluded that the new filter significantly simplifies singular cases and improves the observability characteristics of the system. Results on simulated walking and squatting datasets demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.Comment: IROS 2014 Submission, IEEE/RSJ International Conference on Intelligent Robots and Systems (2014) 952-95

Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid

Hierarchical inverse dynamics based on cascades of quadratic programs have been proposed for the control of legged robots. They have important benefits but to the best of our knowledge have never been implemented on a torque controlled humanoid where model inaccuracies, sensor noise and real-time computation requirements can be problematic. Using a reformulation of existing algorithms, we propose a simplification of the problem that allows to achieve real-time control. Momentum-based control is integrated in the task hierarchy and a LQR design approach is used to compute the desired associated closed-loop behavior and improve performance. Extensive experiments on various balancing and tracking tasks show very robust performance in the face of unknown disturbances, even when the humanoid is standing on one foot. Our results demonstrate that hierarchical inverse dynamics together with momentum control can be efficiently used for feedback control under real robot conditions.Comment: 21 pages, 11 figures, 4 tables in Autonomous Robots (2015

On the Use of Torque Measurement in Centroidal State Estimation

State of the art legged robots are either capable of measuring torque at the output of their drive systems, or have transparent drive systems which enable the computation of joint torques from motor currents. In either case, this sensor modality is seldom used in state estimation. In this paper, we propose to use joint torque measurements to estimate the centroidal states of legged robots. To do so, we project the whole-body dynamics of a legged robot into the nullspace of the contact constraints, allowing expression of the dynamics independent of the contact forces. Using the constrained dynamics and the centroidal momentum matrix, we are able to directly relate joint torques and centroidal states dynamics. Using the resulting model as the process model of an Extended Kalman Filter (EKF), we fuse the torque measurement in the centroidal state estimation problem. Through real-world experiments on a quadruped robot with different gaits, we demonstrate that the estimated centroidal states from our torque-based EKF drastically improve the recovery of these quantities compared to direct computation

Enterotoxigenic Escherichia coli Modulates Host Intestinal Cell Membrane Asymmetry and Metabolic Activity▿

Enterotoxigenic Escherichia coli (ETEC) is a common cause of travelers' and postweaning diarrhea in humans and swine, respectively. The extent to which ETEC damages host cells is unclear. Experiments are presented that probe the ability of porcine ETEC isolates to induce apoptosis and cell death in porcine intestinal epithelial cells. Quantification of host phosphatidylserine exposure following ETEC infection suggested that ETEC induced changes in plasma membrane asymmetry, independent of the expression of the heat-labile enterotoxin. Significant host cell death was not observed. ETEC infection also caused a drastic inhibition of host esterase activity, as measured by calcein fluorescence. While ETEC infection resulted in activation of host caspase 3, terminal deoxynucleotidyltransferase-mediated dUTP-biotin nick end labeling of DNA double-strand breakage, indicative of late stages of apoptosis, was not observed. Camptothecin-induced apoptosis markedly increased subsequent ETEC adherence. Transfer of cell-free supernatants from apoptotic cells to bacterial inocula prior to infection of naïve cells increased the transcriptional activity of the regulatory region upstream of the K88ac operon and promoted subsequent adherence to host cells

Human antibodies for immunotherapy development generated via a human B cell hybridoma technology

Current strategies for the production of therapeutic mAbs include he use of mammalian cell systems to recombinantly produce Abs erived from mice bearing human Ig transgenes humanization of odent Abs or phage libraries. Generation of hybridomas secreting uman mAbs has been previously reported; however this approach as not been fully exploited for immunotherapy development. e previously reported the use of transient regulation of ellular DNA mismatch repair processes to enhance traits (e.g. ffinity and titers) of mAb-producing cell lines including hybridomas. e reasoned that this process named morphogenics could e used to improve suboptimal hybridoma cells generated by eans of ex vivo immunization and immortalization of antigenspecific uman B cells for therapeutic Ab development. Here we resent a platform process that combines hybridoma and morphogenics echnologies for the generation of fully human mAbs pecific for disease-associated human antigens. We were able to enerate hybridoma lines secreting mAbs with high binding specificity nd biological activity. One mAb with strong neutralizing ctivity against human granulocyte–macrophage colony-stimulating actor was identified that is now considered for preclinical evelopment for autoimmune disease indications. Moreover hese hybridoma cells have proven suitable for genetic optimization sing the morphogenics process and have shown potential for arge-scale manufacturing. Originally published Proceedings of the National Academy of Sciences Vol. 103 No. 10 Mar 200