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Nonprehensile Robotic Manipulation: Controllability and Planning
, 1997
"... the author and should not be interpreted as representing the o cial policies, either expressed or A good model of the mechanics of a task is a resource for a robot, just as actuators and sensors are resources. The e ective use of frictional, gravitational, and dynamic forces can substitute for extra ..."
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Cited by 26 (5 self)
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the author and should not be interpreted as representing the o cial policies, either expressed or A good model of the mechanics of a task is a resource for a robot, just as actuators and sensors are resources. The e ective use of frictional, gravitational, and dynamic forces can substitute for extra actuators; the expectation derived from a good model can minimize sensing requirements. Despite this, most robot systems attempt to dominate or nullify task mechanics, rather than exploit them. There has been little e ort to understand the manipulation capabilities of even the simplest robots under more complete mechanics models. This thesis addresses that knowledge de cit by studying graspless or nonprehensile manipulation. Nonprehensile manipulation exploits task mechanics to achieve a goal state without grasping, allowing simple mechanisms to accomplish complex tasks. With nonprehensile manipulation, a robot can manipulate objects too large or heavy to be grasped and lifted, and a lowdegreeoffreedom robot can control more degreesoffreedom of an object by allowing relative motion between the object and the manipulator. Two key problems are determining controllability of and motion planning for
Observing Pose and Motion through Contact
 In Proceedings of the IEEE International Conference on Robotics and Automation
, 1998
"... This paper investigates how to "observe" a planar object being pushed by a finger. The pushing is governed by a nonlinear system that relates through contact the object pose and motion to the finger motion. Nonlinear observability theory is employed to show that the contact information is ..."
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Cited by 15 (2 self)
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This paper investigates how to "observe" a planar object being pushed by a finger. The pushing is governed by a nonlinear system that relates through contact the object pose and motion to the finger motion. Nonlinear observability theory is employed to show that the contact information is often sufficient for the finger to determine not only the pose but also the motion of the object. Therefore a sensing strategy can be realized as an observer of the nonlinear dynamical system, which is subsequently introduced. The observer, based on the result of [6], has its "gain" determined by the solution of a Lyapunovlike equation. Simulations have been done to demonstrate the feasibility of the observer. A sensor has been implemented using strain gauges and mounted on an Adept robot with which preliminary experiments have been conducted. From a general perspective, this work presents an approach for acquiring geometric and dynamical information about a task from a small amount of tactile data, ...
Fast Adaptation for Effectaware Pushing
 in Humanoids
, 2011
"... Abstract — In order to produce robots that are more capable of skilled manipulation tasks, they must learn meaningful knowledge of how objects behave to external stimulus. With this knowledge a robot can predict the outcome of an action, control the object to serve a particular purpose, and together ..."
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Cited by 4 (0 self)
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Abstract — In order to produce robots that are more capable of skilled manipulation tasks, they must learn meaningful knowledge of how objects behave to external stimulus. With this knowledge a robot can predict the outcome of an action, control the object to serve a particular purpose, and together with reasoning, create or modify robot plans. In this paper we 1) build a mathematical compact model for planar sliding motion of an object, 2) show how a robot acquires the parameters of such a model; then how this is used to 3) predict pushing actions; and 4) to move an object from any 1 position and orientation to another. I.
Localization of curved parts through continual touch
 IEEE Transactions on Robotics
"... Abstract—We describe a simple system that localizes twodimensional curved shapes through touch sensing, offering computational and experimental studies. The idea lies in determining the placement of a manipulator on a curved object during some special motion—rolling. A geometric algorithm is introd ..."
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Abstract—We describe a simple system that localizes twodimensional curved shapes through touch sensing, offering computational and experimental studies. The idea lies in determining the placement of a manipulator on a curved object during some special motion—rolling. A geometric algorithm is introduced to locate the boundary segment traced out by their contact using tactile data. Both completeness and local convergence have been established. The algorithm is asymptotically as efficient as evaluating the object’s perimeter through numerical integration. For implementation, a twoaxis force/torque sensor has been designed to realize contact sensing. Functioning like a “wrist, ” the sensor is calibrated over the ratio between the bending and twisting moments, eliminating the need for known weights. A simple geometrybased control strategy is devised to implement the rolling motion. Experiments have been conducted with an Adept Cobra 600 manipulator. Index Terms—Curves, kinematics of rolling, parts localization, solid mechanics, touch sensing. I.
Flexible Part Orienting Using Rotation Direction and Force Measurements
 International Journal of Robotics Research
, 2001
"... This paper presents a novel sensorbased flexible part orienting system based around the commonly available force/torque sensor. The system orients planar parts arriving on a conveyor belt via a sequence of pushing operations with a force/torque sensor equipped fence. A method to use the raw force ..."
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This paper presents a novel sensorbased flexible part orienting system based around the commonly available force/torque sensor. The system orients planar parts arriving on a conveyor belt via a sequence of pushing operations with a force/torque sensor equipped fence. A method to use the raw force data from the sensor to infer the rotation direction of the part is presented. Algorithms utilising (i) only rotation direction and (ii) rotation direction plus force information are presented. These algorithms are shown to find orienting plans with fewer steps than current sensorless orienting techniques, and for a number of specified part shape classes, current sensorbased techniques. Plans generated by our algorithms were tested and verified using a conveyor/robotic car testbed.
Pose Estimation for Contact Manipulation with Manifold Particle Filters
"... Abstract — We investigate the problem of estimating the state of an object during manipulation. Contact sensors provide valuable information about the object state during actions which involve persistent contact, e.g. pushing. However, contact sensing is very discriminative by nature, and therefore ..."
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Abstract — We investigate the problem of estimating the state of an object during manipulation. Contact sensors provide valuable information about the object state during actions which involve persistent contact, e.g. pushing. However, contact sensing is very discriminative by nature, and therefore the set of object states which contact a sensor constitutes a lowerdimensional manifold in the state space of the object. This causes stochastic state estimation methods such as particle filters to perform poorly when contact sensors are used. We propose a new algorithm, the manifold particle filter, which uses dual particles directly sampled from the contact manifold to avoid this problem. The algorithm adapts to the probability of contact by dynamically changing the number of dual particles sampled from the manifold. We compare our algorithm to the particle filter through extensive experiments and we show that our algorithm is both faster and better at estimating the state. Our algorithm’s performance improves with increasing sensor accuracy and the filter’s update rate. We implement the algorithm on a real robot using a force/torque sensor and strain gauges to track the pose of a pushed object.
Manifold representations for state estimation in contact manipulation
 In ISRR
, 2013
"... Abstract We investigate the problem of using contact sensors to estimate the configuration of an object during manipulation. Contact sensing is very discriminative by nature and, therefore, the set of object configurations that activate a sensor constitutes a lowerdimensional manifold in the conf ..."
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Abstract We investigate the problem of using contact sensors to estimate the configuration of an object during manipulation. Contact sensing is very discriminative by nature and, therefore, the set of object configurations that activate a sensor constitutes a lowerdimensional manifold in the configuration space of the object. This causes conventional state estimation methods, such as particle filters, to perform poorly during periods of contact. The manifold particle filter addresses this problem by sampling particles directly from the contact manifold. When it exists, we can sample these particles from an analytic representation of the contact manifold. We present two alternative samplebased contact manifold representations that make no assumptions about the objecthand geometry: rejection sampling and trajectory rollouts. We discuss theoretical considerations behind these three representations and compare their performance in a suite of simulation experiments. We show that all three representations enable the manifold particle filter to outperform the conventional particle filter. Additionally, we show that the trajectory rollout representation performs similarly to the analytic method despite the rollout method’s relative simplicity. 1
Pose Estimation for Planar Contact Manipulation with Manifold Particle Filters
"... We investigate the problem of using contact sensors to estimate the pose of an object during planar pushing by a fixedshape hand. Contact sensors are unique because they inherently discriminate between “contact ” and “nocontact ” configurations. As a result, the set of object configurations that a ..."
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We investigate the problem of using contact sensors to estimate the pose of an object during planar pushing by a fixedshape hand. Contact sensors are unique because they inherently discriminate between “contact ” and “nocontact ” configurations. As a result, the set of object configurations that activates a sensor constitutes a lowerdimensional contact manifold in the configuration space of the object. This causes conventional state estimation methods, such as the particle filter, to perform poorly during periods of contact due to particle starvation. In this paper, we introduce the manifold particle filter as a principled way of solving the state estimation problem when the state moves between multiple manifolds of different dimensionality. The manifold particle filter avoids particle starvation during contact by adaptively sampling particles that reside on the contact manifold from the dual proposal distribution. We describe three techniques—one analytical, and two samplebased—of sampling from the dual proposal distribution and compare their relative strengths and weaknesses. We present simulation results that show that all three techniques outperform the conventional particle filter in both speed and accuracy. Additionally, we implement the manifold particle filter on a real robot and show that it successfully tracks the pose of a pushed object using commercially available tactile sensors. 1
Observing Pose and Motion through Contact
"... This paper investigates how to “observe ” a planar object being pushed by a finger. The pushing is governed by a nonlinear system that relates through contact the object pose and motion to the finger motion. Nonlinear observability theory is employed to show that the contact information is often suf ..."
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This paper investigates how to “observe ” a planar object being pushed by a finger. The pushing is governed by a nonlinear system that relates through contact the object pose and motion to the finger motion. Nonlinear observability theory is employed to show that the contact information is often sufficient for the finger to determine not only the pose but also the motion of the object. Therefore a sensing strategy can be realized as an observer of the nonlinear dynamical system, which is subsequently introduced. The observer, based on the result of [6], has its “gain ” determined by the solution of a Lyapunovlike equation. Simulations have been done to demonstrate the feasibility of the observer. A sensor has been implemented using strain gauges and mounted on an Adept robot with which preliminary experiments have been conducted. From a general perspective, this work presents an approach for acquiring geometric and dynamical information about a task from a small amount of tactile data, with the application of nonlinear observability theory. 1
Observing Pose and Motion through Contact
"... This paper investigates how to “observe ” a planar object being pushed by a finger. The pushing is governed by a nonlinear system that relates through contact the object pose and motion to the finger motion. Nonlinear observability theory is employed to show that the contact information is often suf ..."
Abstract
 Add to MetaCart
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This paper investigates how to “observe ” a planar object being pushed by a finger. The pushing is governed by a nonlinear system that relates through contact the object pose and motion to the finger motion. Nonlinear observability theory is employed to show that the contact information is often sufficient for the finger to determine not only the pose but also the motion of the object. Therefore a sensing strategy can be realized as an observer of the nonlinear dynamical system, which is subsequently introduced. The observer, based on the result of [6], has its “gain ” determined by the solution of a Lyapunovlike equation. Simulations have been done to demonstrate the feasibility of the observer. A sensor has been implemented using strain gauges and mounted on an Adept robot with which preliminary experiments have been conducted. From a general perspective, this work presents an approach for acquiring geometric and dynamical information about a task from a small amount of tactile data, with the application of nonlinear observability theory. 1