Autonomous manipulation with a general-purpose simple hand

Mason, Matthew T.; Rodríguez, Alberto; Srinivasa, Siddhartha S.; Vázquez, Andrés S.

doi:10.1177/0278364911429978

Cited by 68 publications

(45 citation statements)

References 62 publications

(67 reference statements)

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“…For the experiments we use a simple gripper with three rigid fingers compliantly connected to a single motor [42]. The hand is attached to a 6-DOF industrial manipulator (ABB IRB140) with a maximum speed of 2.…”

Section: Resultsmentioning

confidence: 99%

“…This includes the pose of the object relative to the hand, and the configuration of the hand. In this paper, we use a simple hand [42] with three rigid fingers compliantly coupled to a single actuator. Each finger has a single joint, so the grasp pose is encoded as the object pose plus the three finger joint angles.…”

Section: Terminologymentioning

confidence: 99%

“…All actions are implemented by a simple gripper with three fingers compliantly driven by a single actuator [42]. The hand has very limited "intrinsic dexterity".…”

Section: Regrasp Actionsmentioning

confidence: 99%

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Extrinsic dexterity: In-hand manipulation with external forces

Dafle

Rodríguez

Paolini

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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212

109

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Abstract-"In-hand manipulation" is the ability to reposition an object in the hand, for example when adjusting the grasp of a hammer before hammering a nail. The common approach to in-hand manipulation with robotic hands, known as dexterous manipulation [1], is to hold an object within the fingertips of the hand and wiggle the fingers, or walk them along the object's surface. Dexterous manipulation, however, is just one of the many techniques available to the robot. The robot can also roll the object in the hand by using gravity, or adjust the object's pose by pressing it against a surface, or if fast enough, it can even toss the object in the air and catch it in a different pose. All these techniques have one thing in common: they rely on resources extrinsic to the hand, either gravity, external contacts or dynamic arm motions. We refer to them as "extrinsic dexterity".In this paper we study extrinsic dexterity in the context of regrasp operations, for example when switching from a power to a precision grasp, and we demonstrate that even simple grippers are capable of ample in-hand manipulation. We develop twelve regrasp actions, all open-loop and handscripted, and evaluate their effectiveness with over 1200 trials of regrasps and sequences of regrasps, for three different objects (see video [2]). The long-term goal of this work is to develop a general repertoire of these behaviors, and to understand how such a repertoire might eventually constitute a general-purpose in-hand manipulation capability.

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Section: Resultsmentioning

confidence: 99%

Section: Terminologymentioning

confidence: 99%

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Extrinsic dexterity: In-hand manipulation with external forces

Dafle

Rodríguez

Paolini

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

212

109

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“…The experimental setup in Figure 1 consists of a simple gripper [14,17] mounted on a robotic arm that iteratively grasps an object from a bin, estimates the distribution of the pose of the object, computes the probability of success for all available actions, chooses the optimal one, and executes it.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Statistical Framework for Post-Grasp Manipulation

Paolini

Rodríguez

Srinivasa

et al. 2013

Experimental Robotics

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Grasping an object is usually only an intermediate goal for a robotic manipulator. To finish the task, the robot needs to know where the object is in its hand and what action to execute. This paper presents a general statistical framework to address these problems. Given a novel object, the robot learns a statistical model of grasp state conditioned on sensor values. The robot also builds a statistical model of the requirements of the task in terms of grasp state accuracy. Both of these models are constructed by offline experiments. The online process then grasps objects and chooses actions to maximize likelihood of success. This paper describes the framework in detail, and demonstrates its effectiveness experimentally in placing, dropping, and insertion tasks. To construct statistical models, the robot performed over 8000 grasp trials, and over 1000 trials each of placing, dropping and insertion.

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“…Grasp planning can benefit substantially from embodiment, as exemplified by simple under-actuated robotic hands [9,14,15,35]. These hands exploit passive compliance to robustly grasp a variety of objects.…”

Section: ) Computationmentioning

confidence: 99%

Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems

Eppner¹,

Höfer²,

Jonschkowski³

et al.

Robotics: Science and Systems XII

103

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Abstract-We describe the winning entry to the Amazon Picking Challenge. From the experience of building this system and competing in the Amazon Picking Challenge, we derive several conclusions: 1) We suggest to characterize robotic system building along four key aspects, each of them spanning a spectrum of solutions-modularity vs. integration, generality vs. assumptions, computation vs. embodiment, and planning vs. feedback. 2) To understand which region of each spectrum most adequately addresses which robotic problem, we must explore the full spectrum of possible approaches. To achieve this, our community should agree on key aspects that characterize the solution space of robotic systems. 3) For manipulation problems in unstructured environments, certain regions of each spectrum match the problem most adequately, and should be exploited further. This is supported by the fact that our solution deviated from the majority of the other challenge entries along each of the spectra.

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Autonomous manipulation with a general-purpose simple hand

Cited by 68 publications

References 62 publications

Extrinsic dexterity: In-hand manipulation with external forces

Extrinsic dexterity: In-hand manipulation with external forces

A Data-Driven Statistical Framework for Post-Grasp Manipulation

Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems

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