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— This paper combines grasp analysis and manipulation planning techniques to perform fast grasp planning in complex scenes. In much previous work on grasping, the object being grasped is assumed to be the only object in the environment. Hence the grasp quality metrics and grasping strategies developed do not perform well when the object is close to(More)
We present the Constrained Bi-directional Rapidly-Exploring Random Tree (CBiRRT) algorithm for planning paths in configuration spaces with multiple constraints. This algorithm provides a general framework for handling a variety of constraints in manipulation planning including torque limits, constraints on the pose of an object held by a robot, and(More)
We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating(More)
We present a manipulation planning framework that allows robots to plan in the presence of constraints on end-effector pose, as well as other common constraints. The framework has three main components: constraint representation, constraint-satisfaction strategies, and a general planning algorithm. These components come together to create an efficient and(More)
—We present the hardware design, software architecture , and core algorithms of HERB 2.0, a bimanual mobile manipulator developed at the Personal Robotics Lab at Carnegie Mellon University. We have developed HERB 2.0 to perform useful tasks for and with people in human environments. We exploit two key paradigms in human environments: that they have(More)
This paper explores how Cloud Computing can facilitate grasping with shape uncertainty. We consider the most common robot gripper: a pair of thin parallel jaws, and a class of objects that can be modeled as extruded polygons. We model a conservative class of push-grasps that can enhance object alignment. The grasp planning algorithm takes as input an(More)
We explore setting bounds on part tolerances based on an adaptive Cloud-based algorithm to estimate lower bounds on achieving force closure during grasping. We consider the most common robot gripper: a pair of thin parallel jaws, and a conservative class of push-grasps allowing slip that can enhance part alignment for parts that can be modeled as extruded(More)
We present an approach to path planning for manipulators that uses Workspace Goal Regions (WGRs) to specify goal end-effector poses. Instead of specifying a discrete set of goals in the manipulator's configuration space, we specify goals more intuitively as volumes in the manipulator's workspace. We show that WGRs provide a common framework for describing(More)
In this paper we present a framework that allows a human and a robot to perform simultaneous manipulation tasks safely in close proximity. The proposed framework is based on early prediction of the human's motion. The prediction system, which builds on previous work in the area of gesture recognition, generates a prediction of human workspace occupancy by(More)
This paper focuses on recognition and prediction of human reaching motion in industrial manipulation tasks. Several supervised learning methods have been proposed for this purpose, but we seek a method that can build models on-the-fly and adapt to new people and new motion styles as they emerge. Thus, unlike previous work, we propose an unsupervised online(More)