Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approximation techniques, their simulation often deviates from real-world physics, especially in the long term. In this paper, we propose to learn a particle-based simulator for complex control tasks. Combining learning with particle-based systems brings in two major benefits: first, the learned simulator, just like other particle-based systems, acts widely on objects of different materials; second, the particle-based representation poses strong inductive bias for learning: particles of the same type have the same dynamics within. This enables the model to quickly adapt to new environments of unknown dynamics within a few observations. We demonstrate robots achieving complex manipulation tasks using the learned simulator, such as manipulating fluids and deformable foam, with experiments both in simulation and in the real world. Our study helps lay the foundation for robot learning of dynamic scenes with particle-based representations.Comment: Accepted to ICLR 2019. Project Page: http://dpi.csail.mit.edu Video: https://www.youtube.com/watch?v=FrPpP7aW3L

Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201

Morphology and the gradient of a symmetric potential predicts gait transitions of dogs

Gaits and gait transitions play a central role in the movement of animals. Symmetry is thought to govern the structure of the nervous system, and constrain the limb motions of quadrupeds. We quantify the symmetry of dog gaits with respect to combinations of bilateral, fore-aft, and spatio-temporal symmetry groups. We tested the ability of symmetries to model motion capture data of dogs walking, trotting and transitioning between those gaits. Fully symmetric models performed comparably to asymmetric with only a 22% increase in the residual sum of squares and only one-quarter of the parameters. This required adding a spatio-temporal shift representing a lag between fore and hind limbs. Without this shift, the symmetric model residual sum of squares was 1700% larger. This shift is related to (linear regression, n = 5, p = 0.0328) dog morphology. That this symmetry is respected throughout the gaits and transitions indicates that it generalizes outside a single gait. We propose that relative phasing of limb motions can be described by an interaction potential with a symmetric structure. This approach can be extended to the study of interaction of neurodynamic and kinematic variables, providing a system-level model that couples neuronal central pattern generator networks and mechanical models

Evolved embodied phase coordination enables robust quadruped robot locomotion

Overcoming robotics challenges in the real world requires resilient control systems capable of handling a multitude of environments and unforeseen events. Evolutionary optimization using simulations is a promising way to automatically design such control systems, however, if the disparity between simulation and the real world becomes too large, the optimization process may result in dysfunctional real-world behaviors. In this paper, we address this challenge by considering embodied phase coordination in the evolutionary optimization of a quadruped robot controller based on central pattern generators. With this method, leg phases, and indirectly also inter-leg coordination, are influenced by sensor feedback.By comparing two very similar control systems we gain insight into how the sensory feedback approach affects the evolved parameters of the control system, and how the performances differs in simulation, in transferal to the real world, and to different real-world environments. We show that evolution enables the design of a control system with embodied phase coordination which is more complex than previously seen approaches, and that this system is capable of controlling a real-world multi-jointed quadruped robot.The approach reduces the performance discrepancy between simulation and the real world, and displays robustness towards new environments.Comment: 9 page

Propagation Networks for Model-Based Control Under Partial Observation

There has been an increasing interest in learning dynamics simulators for model-based control. Compared with off-the-shelf physics engines, a learnable simulator can quickly adapt to unseen objects, scenes, and tasks. However, existing models like interaction networks only work for fully observable systems; they also only consider pairwise interactions within a single time step, both restricting their use in practical systems. We introduce Propagation Networks (PropNet), a differentiable, learnable dynamics model that handles partially observable scenarios and enables instantaneous propagation of signals beyond pairwise interactions. Experiments show that our propagation networks not only outperform current learnable physics engines in forward simulation, but also achieve superior performance on various control tasks. Compared with existing model-free deep reinforcement learning algorithms, model-based control with propagation networks is more accurate, efficient, and generalizable to new, partially observable scenes and tasks.Comment: Accepted to ICRA 2019. Project Page: http://propnet.csail.mit.edu Video: https://youtu.be/ZAxHXegkz4

An attribute oriented induction based methodology to aid in predictive maintenance: anomaly detection, root cause analysis and remaining useful life

Predictive Maintenance is the maintenance methodology that provides the best performance to industrial organisations in terms of time, equipment effectiveness and economic savings. Thanks to the recent advances in technology, capturing process data from machines and sensors attached to them is no longer a challenging task, and can be used to perform complex analyses to help with maintenance requirements. On the other hand, knowledge of domain experts can be combined with information extracted from the machines’ assets to provide a better understanding of the underlying phenomena. This thesis proposes a methodology to assess the different requirements in relation to Predictive Maintenance. These are (i) Anomaly Detection (AD), (ii) Root Cause Analysis (RCA) and (iii) estimation of Remaining Useful Life (RUL). Multiple machine learning techniques and algorithms can be found in the literature to carry out the calculation of these requirements. In this thesis, the Attribute Oriented Induction (AOI) algorithm has been adopted and adapted to the Predictive Maintenance methodology needs. AOI has the capability of performing RCA, but also possibility to be used as an AD system. With the purpose of performing Predictive Maintenance, a variant, Repetitive Weighted Attribute Oriented Induction (ReWAOI ), has been proposed. ReWAOI has the ability to combine information extracted from the machine with the knowledge of experts in the field to describe its behaviour, and derive the Predictive Maintenance requirements. Through the use of ReWAOI, one-dimensional quantification function from multidimensional data can be obtained. This function is correlated with the evolution of the machine’s wear over time, and thus, the estimation of AD and RUL has been accomplished. In addition, the ReWAOI helps in the description of failure root causes. The proposed contributions of the thesis have been validated in different scenarios, both emulated but also real industrial case studies.Enpresei errendimendu hoberena eskaintzen dien mantentze metodologia Mantentze Prediktiboa da, denbora, ekipamenduen eraginkortasun, eta ekonomia alorretan. Azken urteetan eman diren teknologia aurrerapenei esker, makina eta sensoreetatiko datuen eskuraketa jada ez da erronka, eta manentenimendurako errekerimenduak betetzen laguntzeko analisi konplexuak egiteko erabili daitezke. Bestalde, alorreko jakintsuen ezagutza makinetatik eskuratzen den informazioarekin bateratu daiteke, gertakarien gaineko ulermena hobea izan dadin. Tesi honetan metodologia berri bat proposatzen da, Mantentze Prediktiboarekin lotura duten errekerimenduak betearazten dituena. Ondorengoak dira: (i) Anomalien Detekzioa (AD), (ii) Erro-Kausaren Analisia (RCA), eta (iii) Gainontzeko Bizitza Erabilgarriaren (RUL) estimazioa. Errekerimendu hauen kalkulua burutzeko, ikasketa automatikoko hainbat algoritmo aurkitu daitezke literaturan. Tesi honetan Attribute Oriented Induction (AOI) algoritmoa erabili eta egokitu da Mantentze Prediktiboaren beharretara. AOI-k RCA estimatzeko ahalmena dauka, baina AD kalkulatzeko erabilia izan daiteke baita ere. Mantentze Prediktiboa aplikatzeko helburuarekin, AOI-rentzat aldaera bat proposatu da: Repetitive Weighted Attribute Oriented Induction (ReWAOI ). ReWAOI-k alorreko jakintsuen ezagutza eta makinetatik eskuratutako informazioa bateratzeko ahalmena dauka, makinen portaera deskribatu ahal izateko, eta horrela, Mantentze Prediktiboaren errekerimenduak betetzeko. ReWAOI-ren erabileraren ondorioz, dimentsio bakarreko kuantifikazio funtzioa eskuratu daiteke hainbat dimentsiotako datuetatik. Funztio hau denboran zehar makinak duen higadurarekin erlazionatuta dago, eta beraz, AD eta RUL-aren estimazioak burutu daitezke. Horretaz gain, ReWAOI-k hutsegiteen erro-kausaren deskribapenak eskaintzeko ahalmena dauka. Tesian proposatutako kontribuzioak hainbat erabilpen kasutan balioztatu dira, batzuk emulatuak, eta beste batzuk industria alorreko kasu errealak izanik.El Mantenimiento Predictivo es la metodología de mantenimiento que mejor rendimiento aporta a las organizaciones industriales en cuestiones de tiempo, eficiencia del equipamiento, y rendimiento económico. Gracias a los recientes avances en tecnología, la captura de datos de proceso de máquinas y sensores ya no es un reto, y puede utilizarse para realizar complejos análisis que ayuden con el cumplimiento de los requerimientos de mantenimiento. Por otro lado, el conocimiento de expertos de dominio puede ser combinado con la información extraída de las máquinas para otorgar una mejor comprensión de los fenómenos ocurridos. Esta tesis propone una metodología que cumple con diferentes requerimientos establecidos para el Mantenimiento Predictivo. Estos son (i) la Detección de Anomalías (AD), el Análisis de la Causa-Raíz (RCA) y (iii) la estimación de la Vida Útil Remanente. Pueden encontrarse múltiples técnicas y algoritmos de aprendizaje automático en la literatura para llevar a cabo el cálculo de estos requerimientos. En esta tesis, el algoritmo Attribute Oriented Induction (AOI) ha sido seleccionado y adaptado a las necesidades que establece el Mantenimiento Predictivo. AOI tiene la capacidad de estimar el RCA, pero puede usarse, también, para el cálculo de la AD. Con el propósito de aplicar Mantenimiento Predictivo, se ha propuesto una variante del algoritmo, denominada Repetitive Weighted Attribute Oriented Induction (ReWAOI ). ReWAOI tiene la capacidad de combinar información extraída de la máquina y conocimiento de expertos de área para describir su comportamiento, y así, poder cumplir con los requerimientos del Mantenimiento Predictivo. Mediante el uso de ReWAOI, se puede obtener una función de cuantificación unidimensional, a partir de datos multidimensionales. Esta función está correlacionada con la evolución de la máquina en el tiempo, y por lo tanto, la estimación de AD y RUL puede ser realizada. Además, ReWAOI facilita la descripción de las causas-raíz de los fallos producidos. Las contribuciones propuestas en esta tesis han sido validadas en distintos escenarios, tanto en casos de uso industriales emulados como reales