3D Human Pose Estimation on a Configurable Bed from a Pressure Image

Robots have the potential to assist people in bed, such as in healthcare settings, yet bedding materials like sheets and blankets can make observation of the human body difficult for robots. A pressure-sensing mat on a bed can provide pressure images that are relatively insensitive to bedding materials. However, prior work on estimating human pose from pressure images has been restricted to 2D pose estimates and flat beds. In this work, we present two convolutional neural networks to estimate the 3D joint positions of a person in a configurable bed from a single pressure image. The first network directly outputs 3D joint positions, while the second outputs a kinematic model that includes estimated joint angles and limb lengths. We evaluated our networks on data from 17 human participants with two bed configurations: supine and seated. Our networks achieved a mean joint position error of 77 mm when tested with data from people outside the training set, outperforming several baselines. We also present a simple mechanical model that provides insight into ambiguity associated with limbs raised off of the pressure mat, and demonstrate that Monte Carlo dropout can be used to estimate pose confidence in these situations. Finally, we provide a demonstration in which a mobile manipulator uses our network's estimated kinematic model to reach a location on a person's body in spite of the person being seated in a bed and covered by a blanket.Comment: 8 pages, 10 figure

Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models

With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map. Moreover, most learning algorithms consider a completely data-driven approach, while often useful information on the structure of the robot is available and should be positively exploited. In this work, we present a simple, yet effective, approach for learning the inverse kinematics. We introduce a structured prediction algorithm that combines a data-driven strategy with the model provided by a forward kinematics function -- even when this function is misspecified -- to accurately solve the problem. The proposed approach ensures that predicted joint configurations are well within the robot's constraints. We also provide statistical guarantees on the generalization properties of our estimator as well as an empirical evaluation of its performance on trajectory reconstruction tasks.Comment: Accepted for publication in IEEE Robotics and Automation Letters (2021) and presentation at IEEE International Conference on Robotics and Automation (2021) Updated funding informatio

Structured Prediction for CRISP Inverse Kinematics Learning With Misspecified Robot Models

With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map. Moreover, most learning algorithms consider a completely data-driven approach, while often useful information on the structure of the robot is available and should be positively exploited. In this work, we present a simple, yet effective, approach for learning the inverse kinematics. We introduce a structured prediction algorithm that combines a data-driven strategy with the model provided by a forward kinematics function – even when this function is misspecified – to accurately solve the problem. The proposed approach ensures that predicted joint configurations are well within the robot's constraints. We also provide statistical guarantees on the generalization properties of our estimator as well as an empirical evaluation of its performance on trajectory reconstruction tasks

Aerodynamic reduced-order Volterra model of an ornithopter under high-amplitude flapping

The unsteady aerodynamics of flapping low-aspect-ratio ellipsoidal-wings in ornithopters is analyzed and modeled by the use of three dimensional Computational Fluid Dynamics (CFD) simulations. The range of interest is high amplitude, moderate frequency flapping, and low to moderate angles of attack at Reynolds around 105, where autonomous ornithopters like GRIFFIN are able to perform complex maneuvers such as perching. The results obtained show that the Leading Edge Vortex is produced above a certain Strouhal and angle of attack at downstroke. The frequency response of the aerodynamic loads are compared with the classical analytical models, observing that analytical models based on absence of viscosity and small perturbations are not appropriate for the range of interest. Through the 3D CFD aerodynamic loads database, a finite memory Volterra model is identified in order to predict the characteristics of forces and moments produced by the flapping wing. A good agreement with the 3D CFD simulations has been found by considering a reduced-order model depending on the effective angle of attack of the surrogate airfoil located at 70% of the semi-span at three-quarters chord on the airfoil, in agreement with the literature. Finally, a methodology for validation with a high-accuracy Motion Capture System and without the need of wind tunnel is proposed. As a result the proposed model provides better estimates than classical analytical ones.European Project GRIFFIN ERC Advanced Grant 2017, Action 78824

Structured Machine Learning for Robotics

Machine Learning has become the essential tool for automating tasks that consist in predicting the output associated to a certain input. However many modern algorithms are mainly developed for the simple cases of classification and regression. Structured prediction is the field concerned with predicting outputs consisting of complex objects such as graphs, orientations or sequences. While these objects are often of practical interest, they do not have many of the mathematical properties that allow to design principled and computationally feasible algorithms with traditional techniques. In this thesis we investigate and develop algorithms for learning manifold-valued functions in the context of structured prediction. Differentiable manifolds are a mathematical abstraction used in many domains to describe sets with continuous constraints and non-Euclidean geometric properties. By taking a structured prediction approach we show how to define statistically consistent estimators for predicting elements of a manifold, in constrast to traditional structured predition algorithms that are restricted to output sets with finite cardinality. We introduce a wide range of applications that leverage manifolds structures. Above all, we study the case of the hyperbolic manifold, a space suited for representing hierarchical data. By representing supervised datasets within hyperbolic space we show how it is possible to invent new concepts in a previously known hierarchy and show promising results in hierarchical classification. We also study how modern structured approaches can help with practical robotics tasks, either improving performances in behavioural pipelines or showing more robust predictions for constrained tasks. Specifically, we show how structured prediction can be used to tackle inverse kinematics problems of redundant robots, accounting for the constraints of the robotic joints. We also consider the task of biological motion detection and show that by leveraging the sequence structure of video streams we significantly reduce the latency of the application. Our studies are complemented by empirical evaluations on both synthetic and real data

Machine Learning and System Identification for Estimation in Physical Systems

In this thesis, we draw inspiration from both classical system identification and modern machine learning in order to solve estimation problems for real-world, physical systems. The main approach to estimation and learning adopted is optimization based. Concepts such as regularization will be utilized for encoding of prior knowledge and basis-function expansions will be used to add nonlinear modeling power while keeping data requirements practical.The thesis covers a wide range of applications, many inspired by applications within robotics, but also extending outside this already wide field.Usage of the proposed methods and algorithms are in many cases illustrated in the real-world applications that motivated the research.Topics covered include dynamics modeling and estimation, model-based reinforcement learning, spectral estimation, friction modeling and state estimation and calibration in robotic machining.In the work on modeling and identification of dynamics, we develop regularization strategies that allow us to incorporate prior domain knowledge into flexible, overparameterized models. We make use of classical control theory to gain insight into training and regularization while using tools from modern deep learning. A particular focus of the work is to allow use of modern methods in scenarios where gathering data is associated with a high cost.In the robotics-inspired parts of the thesis, we develop methods that are practically motivated and make sure that they are implementable also outside the research setting. We demonstrate this by performing experiments in realistic settings and providing open-source implementations of all proposed methods and algorithms

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

Unscented Kalman Filter for Brain-Machine Interfaces

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation