We present and evaluate an approach for human-in-the-loop specification of shape reconstruction with annotations for basic robot-object interactions. Our method is based on the idea of model annotation: the addition of simple cues to an underlying object model to specify shape and delineate a simple task. The goal is to explore reducing the complexity of CAD-like interfaces so that novice users can quickly recover an object's shape and describe a manipulation task that is then carried out by a robot. The object modeling and interaction annotation capabilities are tested with a user study and compared against results obtained using existing approaches. The approach has been analyzed using a variety of shape comparison, grasping, and manipulation metrics, and tested with the PR2 robot platform, where it was shown to be successful.
One popular tool for motion planning is Moveit! @cite_113 . This tool can be used within the larger Robot Operating System (ROS) to perform automatic motion planning with collision avoidance. This may also be used in conjunction with a manipulation interface that humans can use to indicate motions. In the work by , @cite_16 , this interface was used to analyze manipulation strategies for indicating grasps and grasp approach methodologies. They compared continuous teleoperation against three other strategies with varying degrees of autonomy. They showed than a combination of manual annotation and automatic processing resulted in the greatest success rate. This is supported by the work done by Hertkorn @cite_129 , where the importance of shared workload is thoroughly analyzed in the context of grasping and manipulation.
@cite_129 @cite_16 @cite_113
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We add to a manipulator's capabilities a new primitive motion which we term a push-grasp. While significant progress has been made in robotic grasping of objects and geometric path planning for manipulation, such work treats the world and the object being grasped as immovable, often declaring failure when simple motions of the object could produce success. We analyze the mechanics of push-grasping and present a quasi-static tool that can be used both for analysis and simulation. We utilize this analysis to derive a fast, feasible motion planning algorithm that produces stable pushgrasp plans for dexterous hands in the presence of object pose uncertainty and high clutter. We demonstrate our algorithm extensively in simulation and on HERB, a personal robotics platform developed at Intel Labs Pittsburgh. Studies of human manipulation strategies suggest that pre-grasp object manipulation, such as rotation or sliding of the object to be grasped, can improve task performance by increasing both the task success rate and the quality of load-supporting postures. In previous demonstrations, pre-grasp object rotation by a robot manipulator was limited to manually-programmed actions. We present a method for automating the planning of pre-grasp rotation for object transport tasks. Our technique optimizes the grasp acquisition point by selecting a target object pose that can be grasped by high-payload manipulator configurations. Careful selection of the transition states leads to successful transport plans for tasks that are otherwise infeasible. In addition, optimization of the grasp acquisition posture also indirectly improves the transport plan quality, as measured by the safety margin of the manipulator payload limits. We present a complete software architecture for reliable grasping of household objects. Our work combines aspects such as scene interpretation from 3D range data, grasp planning, motion planning, and grasp failure identification and recovery using tactile sensors. We build upon, and add several new contributions to the significant prior work in these areas. A salient feature of our work is the tight coupling between perception (both visual and tactile) and manipulation, aiming to address the uncertainty due to sensor and execution errors. This integration effort has revealed new challenges, some of which can be addressed through system and software engineering, and some of which present opportunities for future research. Our approach is aimed at typical indoor environments, and is validated by long running experiments where the PR2 robotic platform was able to consistently grasp a large variety of known and unknown objects. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operating System (ROS). The class of problems that involve finding where to place or how to move a solid object in the presence of obstacles is discussed. The solution to this class of problems is essential to the automatic planning of manipulator transfer movements, i.e., the motions to grasp a part and place it at some destination. For example, planning transfer movements requires the ability to plan paths for the manipulator that avoid collisions with objects in the workspace and the ability to choose safe grasp points on objects. The approach to these problems described here is based on a method of computing an explicit representation of the manipulator configurations that would bring about a collision.