A compliance-centric view of grasping

We advocate the central importance of compliance for grasp performance and demonstrate that grasp algorithms can achieve robust performance by explicitly considering and exploiting mechanical compliance of the grasping hand. Specifically, we consider the problem of robust grasping in the absence of a priori object models, focusing on object capture and grasp stability under variations of object shape for a given robotic hand. We present a simple characterization of the relationship between hand compliance, object shape, and grasp success. Based on this hypothesis, we devise a compliance-centric grasping algorithm. Real-world experiments show that this algorithm outperforms compliance-agnostic grasping, eliminates the need for explicit contact state planning, and simplifies the perceptual requirements when no a priori information about the environment is available.EC/FP7/248258/EU/Flexible Skill Acquisition and Intuitive Robot Tasking for Mobile Manipulation in the Real World/FIRST-M

Évolution thermo-hydro-géochimique d'un puits à colonne permanente conduisant à la précipitation et à la dissolution de la calcite

RÉSUMÉ L’installation d’un puits à colonne permanente, destiné à réduire la consommation énergétique des systèmes de chauffage et de climatisation des bâtiments, dans un aquifère calcaire peut engendrer des problèmes opérationnels. En effet, l’exploitation directe de l’eau souterraine comme fluide caloporteur, ainsi que les fréquentes variations de sa composition chimique favorisent la précipitation de la calcite. La démocratisation de cette technologie est ainsi limitée par un manque de connaissance à ce sujet. De ce fait, l’objectif général de la thèse est de comprendre les mécanismes thermo-hydro-géochimiques, conduisant à la précipitation et la dissolution de la calcite au sein d’un puits à colonne permanente, installé dans un aquifère calcaire fracturé. Les puits à colonne permanente représentent un système complexe, couplant l’écoulement de l’eau souterraine, le transfert de chaleur par advection et conduction, ainsi que le transport réactif multi-espèces dans l’eau. Pour répondre à l’objectif général de la thèse, un modèle numérique d’un puits installé dans un aquifère calcaire fracturé est développé en couplant trois modèles, à savoir un modèle d’écoulement d’eau, un modèle de transfert de chaleur et un modèle géochimique. La vitesse de Darcy, définie par le modèle d’écoulement d’eau, est utilisée par le modèle de transfert de chaleur pour définir la température sur l’ensemble du domaine. Finalement, la vitesse de Darcy et la température sont intégrées dans le modèle géochimique pour simuler le devenir de neuf espèces chimiques considérées---------- ABSTRACT The installation of standing column wells, to reduce the energy consumption for space heating and cooling of buildings, may cause operational problems in a calcareous aquifer. Indeed, the direct use of groundwater as the heat carrier fluid, as well as frequent changes in its chemical composition, favor calcite precipitation. The democratization of this technology is thus limited by a lack of knowledge about this topic. Therefore, the main objective of the thesis is to understand the thermo-hydro-geochemical mechanisms leading to precipitation and dissolution of calcite in a standing column well installed in a fractured calcareous aquifer. Standing column wells form a complex system, coupling groundwater flow, heat transfer by conduction and advection and multi-species reactive transport in water. To meet the main objective of this thesis, a numerical model of a well installed in a fractured calcareous aquifer is developed by coupling three models, namely a groundwater flow model, a heat transfer model and a geochemical model. The Darcy velocity defined by the grounwater flow model is used by the heat transfer model to calculate the temperature throughout the simulation domain. Finally, the Darcy velocity and the temperature are integrated in the geochemical model to simulate the behavior of nine chemical species

CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation

We address the important problem of generalizing robotic rearrangement to clutter without any explicit object models. We first generate over 650K cluttered scenes - orders of magnitude more than prior work - in diverse everyday environments, such as cabinets and shelves. We render synthetic partial point clouds from this data and use it to train our CabiNet model architecture. CabiNet is a collision model that accepts object and scene point clouds, captured from a single-view depth observation, and predicts collisions for SE(3) object poses in the scene. Our representation has a fast inference speed of 7 microseconds per query with nearly 20% higher performance than baseline approaches in challenging environments. We use this collision model in conjunction with a Model Predictive Path Integral (MPPI) planner to generate collision-free trajectories for picking and placing in clutter. CabiNet also predicts waypoints, computed from the scene's signed distance field (SDF), that allows the robot to navigate tight spaces during rearrangement. This improves rearrangement performance by nearly 35% compared to baselines. We systematically evaluate our approach, procedurally generate simulated experiments, and demonstrate that our approach directly transfers to the real world, despite training exclusively in simulation. Robot experiment demos in completely unknown scenes and objects can be found at this http https://cabinet-object-rearrangement.github.i

Exploitation of environmental constraints in human and robotic grasping

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. These constraints, leveraged through motion in contact, counteract uncertainty in state variables relevant to grasp success. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present support for this view found through the analysis of human grasp behavior and by showing robust robotic grasping based on constraint-exploiting grasp strategies. Furthermore, we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints

Probabilistic multi-class segmentation for the Amazon picking challenge

We present a method for multi-class segmentation from RGB-D data in a realistic warehouse picking setting. The method computes pixel-wise probabilities and combines them to find a coherent object segmentation. It reliably segments objects in cluttered scenarios, even when objects are translucent, reflective, highly deformable, have fuzzy surfaces, or consist of loosely coupled components. The robust performance results from the exploitation of problem structure inherent to the warehouse setting. The proposed method proved its capabilities as part of our winning entry to the 2015 Amazon Picking Challenge. We present a detailed experimental analysis of the contribution of different information sources, compare our method to standard segmentation techniques, and assess possible extensions that further enhance the algorithm’s capabilities. We release our software and data sets as open source