Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object.Comment: IEEE Robotics and Automation Letters. Preprint Version. Accepted February, 202

Grasp Affordances from Multi-Fingered Tactile Exploration using Dynamic Potential Fields

In this paper, we address the problem of tactile exploration and subsequent extraction of grasp hypotheses for unknown objects with a multi-fingered anthropomorphic robot hand. We present extensions on our tactile exploration strategy for unknown objects based on a dynamic potential field approach resulting in selective exploration in regions of interest. In the subsequent feature extraction, faces found in the object model are considered to generate grasp affordances. Candidate grasps are validated in a four stage filtering pipeline to eliminate impossible grasps. To evaluate our approach, experiments were carried out in a detailed physics simulation using models of the five-finger hand and the test objects

Constrained motion planning and execution for soft robots

There are many reasons why a compliant robot is expected to perform better than a rigid one in interaction tasks, which include limitation of interaction forces, resilience to modeling errors, robustness, naturalness of motion, and energy efficiency. Most of these reasons are apparent if one thinks of how the human body interacts with its environment. However, most of the work in robotic planning and control of interaction has been traditionally developed for rigid robot models. Indeed, planning and control for compliant robots can be substantially harder. In this thesis, I propose the point of view that the difficulties encountered in planning and control for soft robots are at least in part due to the fact that the same approaches previously used for rigid robots are used as a starting point and adapted. On the opposite, if new methods are considered that start from consideration of compliance from the very beginning, the planning and control problems can be of comparable difficulty, or even substantially simpler, than their rigid counterpart. I will argue this thesis with two main examples. The first part of this thesis presents a new approach to integrate motion planning and control for robots in interaction. One of the peculiarities of interaction tasks is that the robot limbs and the environment form "closed kinematic chains". If rigid models are considered, the dynamics of robots in interaction become constrained, and Differential Algebraic Equations replace Ordinary Differential Equations, i.e. typically a much harder problem to deal with. However, in the thesis I show that this is not necessarily so. Indeed, consideration of compliance allows to have a more tractable mathematical model of interacting systems, and to introduce more sophisticated control approaches. Specifically, we present a novel geometric control scheme under which for constrained robot systems we achieve decoupled interaction control (i.e. make position errors irrelevant to force control, and viceversa). Based on this result, it is possible to decouple the planning problem in two separate aspects. On one side, we make dealing with motion planning of the constrained system easier by relaxing the geometric constraint, i.e. replacing the lower--dimensional constraint manifold with a narrow but full-dimensional boundary layer. This allows us to plan motion using state-of-the-art methods, such as RRT*, on points within the boundary layer, which we can efficiently sample. On the other side we control interaction forces, i.e. forces generated by displacements in the perpendicular direction to the tangent space of the constraint manifold. Thanks to the (locally) noninteracting control characteristic of our scheme, the two controllers can be applied separately and in sequence, so that the interaction force controller can correct for any discrepancies resulting from the boundary layer approximation used in the constrained position controller. The geometric noninteracting controller can be applied both in simulation for planning, and in real time for execution control. Moreover, while it does rely on considering a model of compliance in the system, it does not make any assumption on the amount of compliance in the system - or in other words, it applies equally well to stiff but elastic robots. The final outcome of the two-stage planner is an effective (possibly optimal from RRT*) trajectory that satisfies constraint with arbitrarily good approximation, asymptotically rejecting perturbations coming from sampled displacements. The second part of this thesis is dedicated to study grasp planning for hands that are simple -- in the sense of low number of actuated degrees of freedom -- but soft, i.e. continuously deformable in an infinity of possible shapes through interaction with objects. Once again, the use of such "soft hands" brings about a change of paradigm in grasp planning with respect to classical rigid multi-dof grasp planning, which only apparently makes the problem harder. However, in this thesis I show that thanks to the correct combination of compliance and underactuation of soft hands, together with the set of all possible physical interactions between the hand, the object and the environment, the grasping problem can be redefined. The new definition includes the possible combination of hand-object functional interactions which I address as "Enabling Constraints". The use of Enabling Constraints constitutes a rather new challenge for existing grasping algorithms: adaptation to totally or partially unknown scenes remains a difficult task, toward which only some approaches have been investigated so far. In this thesis I present a first approach to the study of this novel kind of manipulation. It is based on an accurate simulation tool and starts from the considerations that hand compliance can be used to adapt to the shape of the surrounding objects and that rather than considering the environment as and obstacle to avoid, it can be used in turn to functionally shape the hand. I show that thanks to this functionality the problem of generating grasping postures for soft hands can be reduced to grasp basic geometries (e.g. cylinders or boxes) in which the geometry of the object can be decomposed

Evaluation und Weiterentwicklung eines kapazitiven taktilen Näherungssensors

Die vorliegende Arbeit hat die Technologie eines kapazitiven taktilen Näherungssensors zum Thema. Zunächst wird anhand eines existierenden Sensors gezeigt, wie dieser in der Robotik in zwei Aufgabenbereichen gewinnbringend eingesetzt werden kann: in der robusten Manipulation und in der Überwachung des Umfelds des Roboters. Im Bereich der Manipulation werden zwei neue Untergebiete für diese Art von Sensoren erschlossen: die Haptische Exploration und die Telemanipulation. Dann wird diese Technologie in einem neuen Entwurf entscheidend weiterentwickelt, indem ihre Funktionalität erweitert, ihre Integrierbarkeit verbessert und ihre Ortsauflösung erhöht wird. Für den Bereich der Manipulation wird ein Zwei-Backen-Greifer mit vorhandenen Sensormodulen ausgestattet. Eine gradientenbasierte Regelung ermöglicht das berührungslose Ausrichten an Objekten in den sechs Raumfreiheitsgraden. Diese Methode ist Grundlage für die weiteren Methoden der Haptischen Exploration und der Telemanipulation. Die traditionelle Haptische Exploration wird erweitert, indem berührungslose Explorationsschritte eingeführt werden, welche effizient ausgeführt werden können. Die Telemanipulation beinhaltet, dass der Nutzer des Systems eine Kraftrückkopplung spürt, welche mit dem Gradienten, der durch die Näherungssensoren detektiert wird, korrespondiert. Mit dieser Unterstützung kann der Nutzer Objekte effizienter explorieren und greifen. Die Überwachung des Umfelds des Roboters wird realisiert, indem ein End-Effektor mit den vorhandenen Sensormodulen ausgestattet wird. In einem Szenario zur Konturverfolgung bzw. Kollisionsvermeidung wird gezeigt, dass der End-Effektor unvorhergesehene Hindernisse erfolgreich umfahren kann. Im vorgestellten Ansatz wird gezeigt, dass die geschätzte Krümmung der Hindernisfläche für eine prädiktive Regelung verwendet werden kann. Aus der anwendungsbezogenen Evaluation des Sensors werden die Anforderungen des neuen Entwurfs abgeleitet. Der Sensor wird in seiner Funktionalität erweitert, insbesondere mit der Fähigkeit, im beidseitig-kapazitiven Modus zu messen. Dieser Modus verbessert die Robustheit bei der Detektion von nicht leitenden Materialien. Hinsichtlich der Integrierbarkeit wird der Sensor modularisiert, d. h. einzelne Sensoreinheiten sind in der Lage autark zu messen und die Signale zu verarbeiten. Schließlich wird eine flexible Ortsauflösung für den Sensor realisiert, damit dieser situativ eine höhere Ortsauflösung oder eine höhere Empfindlichkeit aufweisen kann. Es wird gezeigt, dass sich die Methoden, welche für den ersten Sensor entwickelt wurden, auch mit dem neuen Sensor umsetzen lassen. Durch die bessere Integrierbarkeit und Vielseitigkeit werden die Voraussetzungen für eine weitere Verbreitung der Technologie geschaffen

Organisation, Repräsentation und Analyse menschlicher Ganzkörperbewegung für die datengetriebene Bewegungsgenerierung bei humanoiden Robotern

Diese Arbeit präsentiert einen Ansatz zur datengetriebenen Bewegungsgenerierung für humanoide Roboter, der auf der Beobachtung und Analyse menschlicher Ganzkörperbewegungen beruht. Hierzu wird untersucht, wie erfasste Bewegungen repräsentiert, klassifiziert und in einer großskaligen Bewegungsdatenbank organisiert werden können. Die statistische Modellierung der Transitionen zwischen charakteristischen Ganzkörperposen ermöglicht im Anschluss die Generierung von Multi-Kontakt-Bewegungen