Vision for Social Robots: Human Perception and Pose Estimation

In order to extract the underlying meaning from a scene captured from the surrounding world in a single still image, social robots will need to learn the human ability to detect different objects, understand their arrangement and relationships relative both to their own parts and to each other, and infer the dynamics under which they are evolving. Furthermore, they will need to develop and hold a notion of context to allow assigning different meanings (semantics) to the same visual configuration (syntax) of a scene. The underlying thread of this Thesis is the investigation of new ways for enabling interactions between social robots and humans, by advancing the visual perception capabilities of robots when they process images and videos in which humans are the main focus of attention. First, we analyze the general problem of scene understanding, as social robots moving through the world need to be able to interpret scenes without having been assigned a specific preset goal. Throughout this line of research, i) we observe that human actions and interactions which can be visually discriminated from an image follow a very heavy-tailed distribution; ii) we develop an algorithm that can obtain a spatial understanding of a scene by only using cues arising from the effect of perspective on a picture of a person’s face; and iii) we define a novel taxonomy of errors for the task of estimating the 2D body pose of people in images to better explain the behavior of algorithms and highlight their underlying causes of error. Second, we focus on the specific task of 3D human pose and motion estimation from monocular 2D images using weakly supervised training data, as accurately predicting human pose will open up the possibility of richer interactions between humans and social robots. We show that when 3D ground-truth data is only available in small quantities, or not at all, it is possible to leverage knowledge about the physical properties of the human body, along with additional constraints related to alternative types of supervisory signals, to learn models that can regress the full 3D pose of the human body and predict its motions from monocular 2D images. Taken in its entirety, the intent of this Thesis is to highlight the importance of, and provide novel methodologies for, social robots' ability to interpret their surrounding environment, learn in a way that is robust to low data availability, and generalize previously observed behaviors to unknown situations in a similar way to humans.</p

Sim2real transfer learning for 3D human pose estimation: motion to the rescue

Synthetic visual data can provide practically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias. However, for many tasks, current models trained on synthetic data generalize poorly to real data. The task of 3D human pose estimation is a particularly interesting example of this sim2real problem, because learning-based approaches perform reasonably well given real training data, yet labeled 3D poses are extremely difficult to obtain in the wild, limiting scalability. In this paper, we show that standard neural-network approaches, which perform poorly when trained on synthetic RGB images, can perform well when the data is pre-processed to extract cues about the person's motion, notably as optical flow and the motion of 2D keypoints. Therefore, our results suggest that motion can be a simple way to bridge a sim2real gap when video is available. We evaluate on the 3D Poses in the Wild dataset, the most challenging modern benchmark for 3D pose estimation, where we show full 3D mesh recovery that is on par with state-of-the-art methods trained on real 3D sequences, despite training only on synthetic humans from the SURREAL dataset.Comment: Accepted at NeurIPS 201

Representation Learning for Robot Keypoint Detection using Prior Kinematic Knowledge

We introduce the Prior Knowledge Robot Keypoint Detection (PK-ROKED) approach for 2D keypoint detection on a robot arm. Our proposed method comprises a Deep Learning network, which learns a representation of keypoints based on prior kinematic knowledge and monocular RGB images. This allows us to provide robust visual feedback for state estimations on a robot arm pose, as this pose can be inaccurate due to imprecise forward kinematics. We incorporate the prior kinematic knowledge about potential keypoint locations into the detection network by concatenating it to the input image. These potential keypoints are derived by forward kinematics, which can be faulty with a bounded error. Hence, this additional information can only indicate and steer the detection algorithm to assumed keypoint locations in image space. Additionally, our approach approximates the uncertainty of a keypoint detection through Monte Carlo Dropout and image moments. To this end, PK-ROKED is trained on the respective synthetic data of a robot arm, which we conduct for two different robot arm models. The resulting performance is evaluated on real-world datasets. We observe our PK-ROKED approach to outperform a baseline network, which we defined for benchmarking. Furthermore, when incorporating our prior knowledge approach into the baseline network we can observe a performance increase compared to without this additional information. To test the robustness of our algorithm, we qualitatively evaluate on challenging data from a space-analogue mission, which demonstrates our approach being potentially deployable in such an environment

Robust Vision-Based Pose Correction for a Robotic Manipulator Using Active Markers

Robots with elastic or lightweight components are becoming common in research, but can suffer from undesired positioning imprecision, which motivates a vision-based pose correction of the manipulator. For robotic manipulators that operate outdoors and under changing illumination conditions, robustness of the vision components is of principal concern. We propose a monocular manipulator pose correction based on active markers which are detected by convergence criteria on the image gradient field. We show the capabilities of the method in several outdoor and indoor experiments, considering the use case of a planetary exploration rover prototype equipped with a lightweight robotic arm. The vision-based manipulator pose correction method proves to be successful despite back light, reflections, and image overexposure and additionally allows continued robot operation in the case of extrinsic camera decalibration

Towards accurate multi-person pose estimation in the wild

In this thesis we are concerned with the problem of articulated human pose estimation and pose tracking in images and video sequences. Human pose estimation is a task of localising major joints of a human skeleton in natural images and is one of the most important visual recognition tasks in the scenes containing humans with numerous applications in robotics, virtual and augmented reality, gaming and healthcare among others. Articulated human pose tracking requires tracking multiple persons in the video sequence while simultaneously estimating full body poses. This task is important for analysing surveillance footage, activity recognition, sports analytics, etc. Most of the prior work focused on the pose estimation of single pre-localised humans whereas here we address a case with multiple people in real world images which entails several challenges such as person-person overlaps in highly crowded scenes, unknown number of people or people entering and leaving video sequences. The first contribution is a multi-person pose estimation algorithm based on the bottom-up detection-by-grouping paradigm. Unlike the widespread top-down approaches our method detects body joints and pairwise relations between them in a single forward pass of a convolutional neural network. Multi-person parsing is performed by optimizing a joint objective based on a multicut graph partitioning framework. Secondly, we extend our pose estimation approach to articulated multi-person pose tracking in videos. Our approach performs multi-target tracking and pose estimation in a holistic manner by optimising a single objective. We further simplify and refine the formulation which allows us to reach close to the real-time performance. Thirdly, we propose a large scale dataset and a benchmark for articulated multi-person tracking. It is the first dataset of video sequences comprising complex multi-person scenes and fully annotated tracks with 2D keypoints. Our fourth contribution is a method for estimating 3D body pose using on-body wearable cameras. Our approach uses a pair of downward facing, head-mounted cameras and captures an entire body. This egocentric approach is free of limitations of traditional setups with external cameras and can estimate body poses in very crowded environments. Our final contribution goes beyond human pose estimation and is in the field of deep learning of 3D object shapes. In particular, we address the case of reconstructing 3D objects from weak supervision. Our approach represents objects as 3D point clouds and is able to learn them with 2D supervision only and without requiring camera pose information at training time. We design a differentiable renderer of point clouds as well as a novel loss formulation for dealing with camera pose ambiguity.In dieser Arbeit behandeln wir das Problem der Schätzung und Verfolgung artikulierter menschlicher Posen in Bildern und Video-Sequenzen. Die Schätzung menschlicher Posen besteht darin die Hauptgelenke des menschlichen Skeletts in natürlichen Bildern zu lokalisieren und ist eine der wichtigsten Aufgaben der visuellen Erkennung in Szenen, die Menschen beinhalten. Sie hat zahlreiche Anwendungen in der Robotik, virtueller und erweiterter Realität, in Videospielen, in der Medizin und weiteren Bereichen. Die Verfolgung artikulierter menschlicher Posen erfordert die Verfolgung mehrerer Personen in einer Videosequenz bei gleichzeitiger Schätzung vollständiger Körperhaltungen. Diese Aufgabe ist besonders wichtig für die Analyse von Video-Überwachungsaufnahmen, Aktivitätenerkennung, digitale Sportanalyse etc. Die meisten vorherigen Arbeiten sind auf die Schätzung einzelner Posen vorlokalisierter Menschen fokussiert, wohingegen wir den Fall mehrerer Personen in natürlichen Aufnahmen betrachten. Dies bringt einige Herausforderungen mit sich, wie die Überlappung verschiedener Personen in dicht gedrängten Szenen, eine unbekannte Anzahl an Personen oder Personen die das Sichtfeld der Video-Sequenz verlassen oder betreten. Der erste Beitrag ist ein Algorithmus zur Schätzung der Posen mehrerer Personen, welcher auf dem Paradigma der Erkennung durch Gruppierung aufbaut. Im Gegensatz zu den verbreiteten Verfeinerungs-Ansätzen erkennt unsere Methode Körpergelenke and paarweise Beziehungen zwischen ihnen in einer einzelnen Vorwärtsrechnung eines faltenden neuronalen Netzwerkes. Die Gliederung in mehrere Personen erfolgt durch Optimierung einer gemeinsamen Zielfunktion, die auf dem Mehrfachschnitt-Problem in der Graphenzerlegung basiert. Zweitens erweitern wir unseren Ansatz zur Posen-Bestimmung auf das Verfolgen mehrerer Personen und deren Artikulation in Videos. Unser Ansatz führt eine Verfolgung mehrerer Ziele und die Schätzung der zugehörigen Posen in ganzheitlicher Weise durch, indem eine einzelne Zielfunktion optimiert wird. Desweiteren vereinfachen und verfeinern wir die Formulierung, was unsere Methode nah an Echtzeit-Leistung bringt. Drittens schlagen wir einen großen Datensatz und einen Bewertungsmaßstab für die Verfolgung mehrerer artikulierter Personen vor. Dies ist der erste Datensatz der Video-Sequenzen von komplexen Szenen mit mehreren Personen beinhaltet und deren Spuren komplett mit zwei-dimensionalen Markierungen der Schlüsselpunkte versehen sind. Unser vierter Beitrag ist eine Methode zur Schätzung von drei-dimensionalen Körperhaltungen mittels am Körper tragbarer Kameras. Unser Ansatz verwendet ein Paar nach unten gerichteter, am Kopf befestigter Kameras und erfasst den gesamten Körper. Dieser egozentrische Ansatz ist frei von jeglichen Limitierungen traditioneller Konfigurationen mit externen Kameras und kann Körperhaltungen in sehr dicht gedrängten Umgebungen bestimmen. Unser letzter Beitrag geht über die Schätzung menschlicher Posen hinaus in den Bereich des tiefen Lernens der Gestalt von drei-dimensionalen Objekten. Insbesondere befassen wir uns mit dem Fall drei-dimensionale Objekte unter schwacher Überwachung zu rekonstruieren. Unser Ansatz repräsentiert Objekte als drei-dimensionale Punktwolken and ist im Stande diese nur mittels zwei-dimensionaler Überwachung und ohne Informationen über die Kamera-Ausrichtung zur Trainingszeit zu lernen. Wir entwerfen einen differenzierbaren Renderer für Punktwolken sowie eine neue Formulierung um mit uneindeutigen Kamera-Ausrichtungen umzugehen