Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Efficient Belief Propagation for Perception and Manipulation in Clutter

Autonomous service robots are required to perform tasks in common human indoor environments. To achieve goals associated with these tasks, the robot should continually perceive, reason its environment, and plan to manipulate objects, which we term as goal-directed manipulation. Perception remains the most challenging aspect of all stages, as common indoor environments typically pose problems in recognizing objects under inherent occlusions with physical interactions among themselves. Despite recent progress in the field of robot perception, accommodating perceptual uncertainty due to partial observations remains challenging and needs to be addressed to achieve the desired autonomy. In this dissertation, we address the problem of perception under uncertainty for robot manipulation in cluttered environments using generative inference methods. Specifically, we aim to enable robots to perceive partially observable environments by maintaining an approximate probability distribution as a belief over possible scene hypotheses. This belief representation captures uncertainty resulting from inter-object occlusions and physical interactions, which are inherently present in clutterred indoor environments. The research efforts presented in this thesis are towards developing appropriate state representations and inference techniques to generate and maintain such belief over contextually plausible scene states. We focus on providing the following features to generative inference while addressing the challenges due to occlusions: 1) generating and maintaining plausible scene hypotheses, 2) reducing the inference search space that typically grows exponentially with respect to the number of objects in a scene, 3) preserving scene hypotheses over continual observations. To generate and maintain plausible scene hypotheses, we propose physics informed scene estimation methods that combine a Newtonian physics engine within a particle based generative inference framework. The proposed variants of our method with and without a Monte Carlo step showed promising results on generating and maintaining plausible hypotheses under complete occlusions. We show that estimating such scenarios would not be possible by the commonly adopted 3D registration methods without the notion of a physical context that our method provides. To scale up the context informed inference to accommodate a larger number of objects, we describe a factorization of scene state into object and object-parts to perform collaborative particle-based inference. This resulted in the Pull Message Passing for Nonparametric Belief Propagation (PMPNBP) algorithm that caters to the demands of the high-dimensional multimodal nature of cluttered scenes while being computationally tractable. We demonstrate that PMPNBP is orders of magnitude faster than the state-of-the-art Nonparametric Belief Propagation method. Additionally, we show that PMPNBP successfully estimates poses of articulated objects under various simulated occlusion scenarios. To extend our PMPNBP algorithm for tracking object states over continuous observations, we explore ways to propose and preserve hypotheses effectively over time. This resulted in an augmentation-selection method, where hypotheses are drawn from various proposals followed by the selection of a subset using PMPNBP that explained the current state of the objects. We discuss and analyze our augmentation-selection method with its counterparts in belief propagation literature. Furthermore, we develop an inference pipeline for pose estimation and tracking of articulated objects in clutter. In this pipeline, the message passing module with the augmentation-selection method is informed by segmentation heatmaps from a trained neural network. In our experiments, we show that our proposed pipeline can effectively maintain belief and track articulated objects over a sequence of observations under occlusion.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163159/1/kdesingh_1.pd

System Architectures for Cooperative Teams of Unmanned Aerial Vehicles Interacting Physically with the Environment

Unmanned Aerial Vehicles (UAVs) have become quite a useful tool for a wide range of applications, from inspection & maintenance to search & rescue, among others. The capabilities of a single UAV can be extended or complemented by the deployment of more UAVs, so multi-UAV cooperative teams are becoming a trend. In that case, as di erent autopilots, heterogeneous platforms, and application-dependent software components have to be integrated, multi-UAV system architectures that are fexible and can adapt to the team's needs are required. In this thesis, we develop system architectures for cooperative teams of UAVs, paying special attention to applications that require physical interaction with the environment, which is typically unstructured. First, we implement some layers to abstract the high-level components from the hardware speci cs. Then we propose increasingly advanced architectures, from a single-UAV hierarchical navigation architecture to an architecture for a cooperative team of heterogeneous UAVs. All this work has been thoroughly tested in both simulation and eld experiments in di erent challenging scenarios through research projects and robotics competitions. Most of the applications required physical interaction with the environment, mainly in unstructured outdoors scenarios. All the know-how and lessons learned throughout the process are shared in this thesis, and all relevant code is publicly available.Los vehículos aéreos no tripulados (UAVs, del inglés Unmanned Aerial Vehicles) se han convertido en herramientas muy valiosas para un amplio espectro de aplicaciones, como inspección y mantenimiento, u operaciones de rescate, entre otras. Las capacidades de un único UAV pueden verse extendidas o complementadas al utilizar varios de estos vehículos simultáneamente, por lo que la tendencia actual es el uso de equipos cooperativos con múltiples UAVs. Para ello, es fundamental la integración de diferentes autopilotos, plataformas heterogéneas, y componentes software -que dependen de la aplicación-, por lo que se requieren arquitecturas multi-UAV que sean flexibles y adaptables a las necesidades del equipo. En esta tesis, se desarrollan arquitecturas para equipos cooperativos de UAVs, prestando una especial atención a aplicaciones que requieran de interacción física con el entorno, cuya naturaleza es típicamente no estructurada. Primero se proponen capas para abstraer a los componentes de alto nivel de las particularidades del hardware. Luego se desarrollan arquitecturas cada vez más avanzadas, desde una arquitectura de navegación para un único UAV, hasta una para un equipo cooperativo de UAVs heterogéneos. Todo el trabajo ha sido minuciosamente probado, tanto en simulación como en experimentos reales, en diferentes y complejos escenarios motivados por proyectos de investigación y competiciones de robótica. En la mayoría de las aplicaciones se requería de interacción física con el entorno, que es normalmente un escenario en exteriores no estructurado. A lo largo de la tesis, se comparten todo el conocimiento adquirido y las lecciones aprendidas en el proceso, y el código relevante está publicado como open-source

Toward Robots with Peripersonal Space Representation for Adaptive Behaviors

The abilities to adapt and act autonomously in an unstructured and human-oriented environment are necessarily vital for the next generation of robots, which aim to safely cooperate with humans. While this adaptability is natural and feasible for humans, it is still very complex and challenging for robots. Observations and findings from psychology and neuroscience in respect to the development of the human sensorimotor system can inform the development of novel approaches to adaptive robotics. Among these is the formation of the representation of space closely surrounding the body, the Peripersonal Space (PPS) , from multisensory sources like vision, hearing, touch and proprioception, which helps to facilitate human activities within their surroundings. Taking inspiration from the virtual safety margin formed by the PPS representation in humans, this thesis first constructs an equivalent model of the safety zone for each body part of the iCub humanoid robot. This PPS layer serves as a distributed collision predictor, which translates visually detected objects approaching a robot\u2019s body parts (e.g., arm, hand) into the probabilities of a collision between those objects and body parts. This leads to adaptive avoidance behaviors in the robot via an optimization-based reactive controller. Notably, this visual reactive control pipeline can also seamlessly incorporate tactile input to guarantee safety in both pre- and post-collision phases in physical Human-Robot Interaction (pHRI). Concurrently, the controller is also able to take into account multiple targets (of manipulation reaching tasks) generated by a multiple Cartesian point planner. All components, namely the PPS, the multi-target motion planner (for manipulation reaching tasks), the reaching-with-avoidance controller and the humancentred visual perception, are combined harmoniously to form a hybrid control framework designed to provide safety for robots\u2019 interactions in a cluttered environment shared with human partners. Later, motivated by the development of manipulation skills in infants, in which the multisensory integration is thought to play an important role, a learning framework is proposed to allow a robot to learn the processes of forming sensory representations, namely visuomotor and visuotactile, from their own motor activities in the environment. Both multisensory integration models are constructed with Deep Neural Networks (DNNs) in such a way that their outputs are represented in motor space to facilitate the robot\u2019s subsequent actions