Sequential trajectory re-planning with tactile information gain for dexterous grasping under object-pose uncertainty

Zito, Claudio; Kopicki, Marek; Stolkin, Rustam; Borst, Christoph; Schmidt, Florian; Roa, Máximo A.; Wyatt, Jeremy L.

doi:10.1109/iros.2013.6696930

Cited by 19 publications

(19 citation statements)

References 11 publications

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“…There is also rapidly growing interest in estimation of object shapes and contacts using force-torque and tactile sensors, [9], [10], [11], [12], [13], [14], [15]. Recent work, [16], [17] has also begun exploring the fusion of visual and tactile data. In [18] a quadrupedal robot estimates the inclination of a planar surface on which it is trotting, by fusing data from IMU accelerometers with optical force sensors at each foot and the kinematics of each leg.…”

Section: Introductionmentioning

confidence: 99%

Kinematics-based estimation of contact constraints using only proprioception

Ortenzi

Lin

Azad

et al. 2016

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)

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Robots are increasingly being required to perform tasks which involve contacts with the environment. This paper addresses the problem of estimating environmental constraints on the robot's motion. We present a method which estimates such constraints, by computing the null space of a set of velocity vectors which differ from commanded velocities during contacts. We further extend this method to handle unilateral constraints, for example when the robot touches a rigid surface. Unlike previous work, our method is based on kinematics analysis, using only proprioceptive joint encoders, thus there is no need for either expensive force-torque sensors or tactile sensors at the contact points or any use of vision. We first show results of experiments with a simulated robot in a variety of situations, and we analyse the effect of various levels of observation noise on the resulting contact estimates. Finally we evaluate the performance of our method on two sets of experiments using a KUKA LWR IV manipulator, tasked with exploring and estimating the constraints caused by a horizontal surface and an inclined surface.

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Section: Introductionmentioning

confidence: 99%

Kinematics-based estimation of contact constraints using only proprioception

Ortenzi

Lin

Azad

et al. 2016

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)

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“…Strategies include statistical techniques [11], grasp selection using object shape and local features [12], decision-theoretic approaches [13], and planning in beliefspace with additional information gain and replanning during the reaching phase [14], [15]. An example of a complete software architecture for reliably grasping household items with a PR2 jaw gripper while dealing with uncertainty is given in [16].…”

Section: Related Workmentioning

confidence: 99%

Changing pre-grasp strategies with increasing object location uncertainty

Illing

Asfour

Pollard

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Successful and robust grasping for humanoid robots is still an ongoing research topic in robotics. Applying human-inspired grasping strategies does not only correspond with more natural looking motions but can also yield good results regarding task success when having to deal with uncertainty. This study investigates human high-level grasping strategies and how they tend to change for different objects when the uncertainty of object location or orientation increases in between two grasps. We are especially interested in potential gains for humanoid robots in a common household setting. By analyzing collected data from human subject grasp experiments with a set of typical objects found in people's homes, we get better insight into how humans handle uncertainty, as well as when and how they change their applied pre-grasp strategy. By adapting the by far most often observed change from a direct grasp attempt to a tapping strategy when dealing with high uncertainty, we can demonstrate a substantial increase of grasp success rate for our robot system with a Shadow Dexterous Hand mounted on a Motoman SDA10 robot while using less than two hand correction steps on average.

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“…6 There is a variety of work on tactile ex ploration for robot manipulation. [7][8][9][10][11][12][13] The goal of this paper is to extend the set of available techniques for guiding tactile exploration to recover surface shape.…”

Section: Introductionmentioning

confidence: 99%

GPAtlasRRT: A Local Tactile Exploration Planner for Recovering the Shape of Novel Objects

Rosales

Spinelli

Gabiccini

et al. 2018

Int. J. Human. Robot.

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Touch is an important modality to recover object shape. We present a method for a robot to complete a partial shape model by local tactile exploration. In local tactile exploration, the finger is constrained to follow the local surface. This is useful for recovering information about a contiguous portion of the object and is frequently employed by humans. There are three contributions. First, we show how to segment an initial point cloud of a grasped, unknown object into hand and object. Second, we present a local tactile exploration planner. This combines a Gaussian Process (GP) model of the object surface with an AtlasRRT planner. The GP predicts the unexplored surface and the uncertainty of that prediction. The AtlasRRT creates a tactile exploration path across this predicted surface, driving it towards the region of greatest uncertainty. Finally, we experimentally compare the planner with alternatives in simulation, and demonstrate the complete approach on a real robot. We show that our planner successfully traverses the object, and that the full object shape can be recovered with a good degree of accuracy.

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Sequential trajectory re-planning with tactile information gain for dexterous grasping under object-pose uncertainty

Cited by 19 publications

References 11 publications

Kinematics-based estimation of contact constraints using only proprioception

Kinematics-based estimation of contact constraints using only proprioception

Changing pre-grasp strategies with increasing object location uncertainty

GPAtlasRRT: A Local Tactile Exploration Planner for Recovering the Shape of Novel Objects

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