Hierarchical Salient Object Detection for Assisted Grasping

Visual scene decomposition into semantic entities is one of the major challenges when creating a reliable object grasping system. Recently, we introduced a bottom-up hierarchical clustering approach which is able to segment objects and parts in a scene. In this paper, we introduce a transform from such a segmentation into a corresponding, hierarchical saliency function. In comprehensive experiments we demonstrate its ability to detect salient objects in a scene. Furthermore, this hierarchical saliency defines a most salient corresponding region (scale) for every point in an image. Based on this, an easy-to-use pick and place manipulation system was developed and tested exemplarily.Comment: Accepted for ICRA 201

Autonomous task-based grasping for mobile manipulators

A fully integrated grasping system for a mobile manipulator to grasp an unknown object of interest (OI) in an unknown environment is presented. The system autonomously scans its environment, models the OI, plans and executes a grasp, while taking into account base pose uncertainty and obstacles in its way to reach the object. Due to inherent line of sight limitations in sensing, a single scan of the OI often does not reveal enough information to complete grasp analysis; as a result, our system autonomously builds a model of an object via multiple scans from different locations until a grasp can be performed. A volumetric next-best-view (NBV) algorithm is used to model an arbitrary object and terminates modelling when grasp poses are discovered on a partially observed object. Two key sets of experiments are presented: i) modelling and registration error in the OI point cloud model is reduced by selecting viewpoints with more scan overlap, and ii) model construction and grasps are successfully achieved while experiencing base pose uncertainty. A generalized algorithm is presented to discover grasp pose solutions for multiple grasp types for a multi-fingered mechanical gripper using sensed point clouds. The algorithm introduces two key ideas: 1) a histogram of finger contact normals is used to represent a grasp “shape” to guide a gripper orientation search in a histogram of object(s) surface normals, and 2) voxel grid representations of gripper and object(s) are cross-correlated to match finger contact points, i.e. grasp “size”, to discover a grasp pose. Constraints, such as collisions with neighbouring objects, are incorporated in the cross-correlation computation. Simulations and preliminary experiments show that 1) grasp poses for three grasp types are found in near real-time, 2) grasp pose solutions are consistent with respect to voxel resolution changes for both partial and complete point cloud scans, 3) a planned grasp pose is executed with a mechanical gripper, and 4) grasp overlap is presented as a feature to identify regions on a partial object model ideal for object transfer or securing an object

Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery

State-of-the-art domestic robot assistants are essentially autonomous mobile manipulators capable of exerting human-scale precision grasps. To maximize utility and economy, non-technical end-users would need to be nearly as efficient as trained roboticists in control and collaboration of manipulation task behaviors. However, it remains a significant challenge given that many WIMP-style tools require superficial proficiency in robotics, 3D graphics, and computer science for rapid task modeling and recovery. But research on robot-centric collaboration has garnered momentum in recent years; robots are now planning in partially observable environments that maintain geometries and semantic maps, presenting opportunities for non-experts to cooperatively control task behavior with autonomous-planning agents exploiting the knowledge. However, as autonomous systems are not immune to errors under perceptual difficulty, a human-in-the-loop is needed to bias autonomous-planning towards recovery conditions that resume the task and avoid similar errors. In this work, we explore interactive techniques allowing non-technical users to model task behaviors and perceive cooperatively with a service robot under robot-centric collaboration. We evaluate stylus and touch modalities that users can intuitively and effectively convey natural abstractions of high-level tasks, semantic revisions, and geometries about the world. Experiments are conducted with \u27pick-and-place\u27 tasks in an ideal \u27Blocks World\u27 environment using a Kinova JACO six degree-of-freedom manipulator. Possibilities for the architecture and interface are demonstrated with the following features; (1) Semantic \u27Object\u27 and \u27Location\u27 grounding that describe function and ambiguous geometries (2) Task specification with an unordered list of goal predicates, and (3) Guiding task recovery with implied scene geometries and trajectory via symmetry cues and configuration space abstraction. Empirical results from four user studies show our interface was much preferred than the control condition, demonstrating high learnability and ease-of-use that enable our non-technical participants to model complex tasks, provide effective recovery assistance, and teleoperative control

\u3cem\u3eGRASP News\u3c/em\u3e, Volume 8, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory. Edited by Thomas Lindsay