Textile Taxonomy and Classification Using Pulling and Twisting

Identification of textile properties is an important milestone toward advanced robotic manipulation tasks that consider interaction with clothing items such as assisted dressing, laundry folding, automated sewing, textile recycling and reusing. Despite the abundance of work considering this class of deformable objects, many open problems remain. These relate to the choice and modelling of the sensory feedback as well as the control and planning of the interaction and manipulation strategies. Most importantly, there is no structured approach for studying and assessing different approaches that may bridge the gap between the robotics community and textile production industry. To this end, we outline a textile taxonomy considering fiber types and production methods, commonly used in textile industry. We devise datasets according to the taxonomy, and study how robotic actions, such as pulling and twisting of the textile samples, can be used for the classification. We also provide important insights from the perspective of visualization and interpretability of the gathered data

Safety Aspects of Data-Driven Control in Contact-Rich Manipulation

A crucial step towards robot autonomy-in environments other than the strictly regulated industrial ones-is to create controllers capable of adapting to diverse conditions. Human-centric environments are filled with a plethora of objects with very distinct properties that can still be manipulated without the need to painstakingly model the interaction dynamics. Furthermore, we do not need an explicit model to safely complete our tasks; rather, we rely on our intuition about the evolution of the interaction that is built upon multiple repetitions of the same task.Accurately translating this ability in how we control our robots in contact-rich tasks is almost infeasible if we rely on controllers that operate based on analytical models of the contacts. Instead, it is advantageous to utilize data-driven techniques that approximate the models based on interactions, much like humans do, and encompass the varying dynamics with a single model. However, for this to be a feasible alternative, we need to consider the safety aspects that occur when we move away from rigorous mathematical models and replace them with approximate data-driven ones. This thesis identifies three safety aspects of data-driven control in contact-rich manipulation: good predictive performance, increased interpretability for the models, and explicit consideration of safe inputs in the face of modelling errors or uninterpretable predictions. The first point is addressed through a model-training scheme that improves the long-term predictions in a food cutting task. In the experiments it is shown that models trained this way are able to adapt to different dynamics efficiently and their prediction error scales better with longer horizons. The second point is addressed by introducing a framework that allows the evaluation of data-driven classification models based on interpretability techniques. The interpretation of the model decisions helps to anticipate failure cases before the model is deployed on the robot, as well as to understand what the models have learned. Finally, the third point is addressed by learning sets of safe states through data. These safe sets are then used to avoid dangerous control inputs in a control scheme that is flexible and adapts to dynamic variations while effectively encouraging the safety of the system.QC 20220203</p

Safety Aspects of Data-Driven Control in Contact-Rich Manipulation

A crucial step towards robot autonomy-in environments other than the strictly regulated industrial ones-is to create controllers capable of adapting to diverse conditions. Human-centric environments are filled with a plethora of objects with very distinct properties that can still be manipulated without the need to painstakingly model the interaction dynamics. Furthermore, we do not need an explicit model to safely complete our tasks; rather, we rely on our intuition about the evolution of the interaction that is built upon multiple repetitions of the same task.Accurately translating this ability in how we control our robots in contact-rich tasks is almost infeasible if we rely on controllers that operate based on analytical models of the contacts. Instead, it is advantageous to utilize data-driven techniques that approximate the models based on interactions, much like humans do, and encompass the varying dynamics with a single model. However, for this to be a feasible alternative, we need to consider the safety aspects that occur when we move away from rigorous mathematical models and replace them with approximate data-driven ones. This thesis identifies three safety aspects of data-driven control in contact-rich manipulation: good predictive performance, increased interpretability for the models, and explicit consideration of safe inputs in the face of modelling errors or uninterpretable predictions. The first point is addressed through a model-training scheme that improves the long-term predictions in a food cutting task. In the experiments it is shown that models trained this way are able to adapt to different dynamics efficiently and their prediction error scales better with longer horizons. The second point is addressed by introducing a framework that allows the evaluation of data-driven classification models based on interpretability techniques. The interpretation of the model decisions helps to anticipate failure cases before the model is deployed on the robot, as well as to understand what the models have learned. Finally, the third point is addressed by learning sets of safe states through data. These safe sets are then used to avoid dangerous control inputs in a control scheme that is flexible and adapts to dynamic variations while effectively encouraging the safety of the system.QC 20220203</p

Modelling and Learning Dynamics for Robotic Food-Cutting

Interaction dynamics are difficult to model analytically, making data-driven controllers preferable for contact-rich manipulation tasks. In this work, we approximate the intricate dynamics of food-cutting with a Long Short-Term Memory (LSTM) model to apply a Model Predictive Controller (MPC). We propose a problem formulation that allows velocity-controlled robots to learn the interaction dynamics and tackle the difficulty of multi-step predictions by training the model with a horizon curriculum. We experimentally demonstrate that our approach leads to good predictive performance that scales for longer prediction horizons, generalizes to unseen object classes and results in controller behaviors with an understanding of the cutting dynamics.QC 20220125</p

Numerical simulation of self-ignition in supersonic turbulent shear flow

Cette étude est consacrée à l’analyse des écoulements réactifs supersoniques cisailléset, plus particulièrement, des couches de mélange compressibles pouvant se développerdans les moteurs ramjet et scramjet. Des méthodes numériques appropriées ont été implémentéeset vérifiées pour aboutir au développement d’un code de calcul numériquemassivement parallèle, appelé CREAMS (compressible reactive multi-species solver). Cedernier a été spécialement conçu pour conduire des simulations numériques haute précision(simulations numériques directes ou DNS) de ce type d’écoulements. Une attentionparticulière a été portée à la description des termes de transport moléculaire et des termessources chimiques de façon à considérer la description physique la plus fidèle possible desmélanges des gaz réactifs à haute vitesse, au sein desquelles les temps caractéristiqueschimiques et de mélange aux petites échelles sont susceptibles d’être du même ordre degrandeur. Les simulations des couches de mélange bidimensionnelles et tridimensionnelles,inertes et réactives, confirment l’importance des effets associés à la compressibilité et autaux de dégagement de chaleur. Les résultats ainsi obtenus diffèrent en certains points deceux issus d’autres simulations qui introduisaient certaines hypothèses simplificatrices :développement temporel, emploi d’une chimie globale ou encore lois de transport simplifiées.En revanche, ils reproduisent certains tendances déjà observées dans un certainnombre d’études expérimentales conduites dans des conditions similaires.This study is devoted to the analysis of supersonic reactive shear flows and, in particular,compressible mixing layers that can develop inside the ramjet and scramjet engines.Appropriate numerical methods have been implemented and tested to achieve the developmentof a massively parallel numerical solver, called CREAMS (compressible reactivemulti-species solver). This tool was designed to conduct high-precision numerical simulations(direct numerical simulations or DNS) of such flows. Particular attention waspaid to the description of the molecular transport terms and chemical source terms toconsider the most accurate physical description of reactive gas mixtures at high velocity,in which the chemical and mixing time scales, corresponding to the smallest scalesof the flow, are susceptible to be of the same order of magnitude. Simulations of twoandthree-dimensional, inert and reactive, mixing layers confirm the importance of theeffects associated with compressibility and rate of heat release. The results obtained differin some points from other simulations which introduced simplifying assumptions such astemporal development, use of a global chemistry or a simplified description of the moleculartransport terms. Nevertheless, they reproduce some trends already observed in severalexperimental studies conducted under similar conditions

Safe Data-Driven Contact-Rich Manipulation

In this paper, we address the safety of data-driven control for contact-rich manipulation. We propose to restrict the controller’s action space to keep the system in a set of safe states. In the absence of an analytical model, we show how Gaussian Processes (GP) can be used to approximate safe sets. We disable inputs for which the predicted states are likely to be unsafe using the GP. Furthermore, we show how locally designed feedback controllers can be used to improve the execution precision in the presence of modelling errors. We demonstrate the benefits of our method on a pushing task with a variety of dynamics, by using known and unknown surfaces and different object loads. Our results illustrate that the proposed approach significantly improves the performance and safety of the baseline controller.QC 20210607No duplikate with DiVA:1631253</p

Safe Data-Driven Contact-Rich Manipulation

In this paper, we address the safety of data-driven control for contact-rich manipulation. We propose to restrict the controller’s action space to keep the system in a set of safe states. In the absence of an analytical model, we show how Gaussian Processes (GP) can be used to approximate safe sets. We disable inputs for which the predicted states are likely to be unsafe using the GP. Furthermore, we show how locally designed feedback controllers can be used to improve the execution precision in the presence of modelling errors. We demonstrate the benefits of our method on a pushing task with a variety of dynamics, by using known and unknown surfaces and different object loads. Our results illustrate that the proposed approach significantly improves the performance and safety of the baseline controller.QC 20210607</p

Safe Data-Driven Model Predictive Control of Systems with Complex Dynamics

In this paper, we address the safety and efficiency of data-driven model predictive controllers (DD-MPC) for systems with complex dynamics. First, we utilize safe exploration of dynamical systems to learn an accurate model for the DD-MPC. During training, we use rapidly exploring random trees (RRT) to collect a uniform distribution of data points in the state-input space and overcome the common distribution shift in model learning. This model is also used to construct a tree offline, which at test time is used in the cost function to provide an estimate of the predicted states' distance to the target. Additionally, we show how safe sets can be approximated using demonstrations of exclusively safe trajectories, i.e. positive examples. During test time, the distances of the predicted trajectories to the safe set are used as a cost term to encourage safe inputs. We use a \emph{broken} version of the inverted pendulum problem where the friction abruptly changes in certain regions as a running example. Our results show that the proposed exploration algorithm and the two proposed cost terms lead to a controller that can effectively avoid unsafe states and displays higher success rates than the baseline controllers with models from controlled demonstrations and even random actions.QC 20211221</p

Interpretability in Contact-Rich Manipulation via Kinodynamic Images

Deep Neural Networks (NNs) have been widely utilized in contact-rich manipulation tasks to model the complicated contact dynamics. However, NN-based models are often difficult to decipher which can lead to seemingly inexplicable behaviors and unidentifiable failure cases. In this work, we address the interpretability of NN-based models by introducing the kinodynamic images. We propose a methodology that creates images from kinematic and dynamic data of contact-rich manipulation tasks. By using images as the state representation, we enable the application of interpretability modules that were previously limited to vision-based tasks. We use this representation to train a Convolutional Neural Network (CNN) and we extract interpretations with Grad-CAM to produce visual explanations. Our method is versatile and can be applied to any classification problem in manipulation tasks to visually interpret which parts of the input drive the model’s decisions and distinguish its failure modes, regardless of the features used. Our experiments demonstrate that our method enables detailed visual inspections of sequences in a task, and high-level evaluations of a model’s behavior.Part of proceedings: ISBN 978-1-7281-9077-8QC 20220503</p