Building an autonomous humanoid tool user

Bluethmann, William; Ambrose, Robert O.; Diftler, Myron; Huber, Eric; Fagg, Andrew H.; Rosenstein, Michael T.; Platt, Robert W.; Grupen, Roderic A.; Breazeal, Cynthia; Brööks, Andrëw G.; Lockerd, Andrea; Peters, Richard Alan; Jenkins, Odest Chadwicke; Matarić, Maja J.; Bugajska, Magdalena D.

doi:10.1109/ichr.2004.1442134

Cited by 20 publications

(14 citation statements)

References 15 publications

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“…The human cooperation aspect makes the task an HRI behavior, and the sensors in the hand provide a tactile element, but since the robot does not touch the human, the work falls outside of what we consider here to be tactile HRI. Another example is to have a human demonstrate a behavior through non-tactile means, for example teleoperation via remote joystick, to a robot that uses tactile sensors to perform the behavior learned from these demonstrations [64,65]. Again, the human-robot interaction is decoupled from the tactile interaction, and so we considered such work to be beyond the scope of this review.…”

Section: Scope Of This Reviewmentioning

confidence: 99%

A survey of Tactile Human–Robot Interactions

Argall

Billard

2010

Robotics and Autonomous Systems

300

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a b s t r a c tRobots come into physical contact with humans in both experimental and operational settings. Many potential factors motivate the detection of human contact, ranging from safe robot operation around humans, to robot behaviors that depend on human guidance. This article presents a review of current research within the field of Tactile Human-Robot Interactions (Tactile HRI), where physical contact from a human is detected by a robot during the execution or development of robot behaviors. Approaches are presented from two viewpoints: the types of physical interactions that occur between the human and robot, and the types of sensors used to detect these interactions. We contribute a structure for the categorization of Tactile HRI research within each viewpoint. Tactile sensing techniques are grouped into three categories, according to what covers the sensors: (i) a hard shell, (ii) a flexible substrate or (iii) no covering. Three categories of physical HRI likewise are identified, consisting of contact that (i) interferes with robot behavior execution, (ii) contributes to behavior execution and (iii) contributes to behavior development. We populate each category with the current literature, and furthermore identify the stateof-the-art within categories and promising areas for future research.

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Section: Scope Of This Reviewmentioning

confidence: 99%

A survey of Tactile Human–Robot Interactions

Argall

Billard

2010

Robotics and Autonomous Systems

300

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“…In RCC, an active or passive compliance mechanism is developed that causes the peg to slide into the hole when a downward force is applied. Alternatively, it is possible to perceive hole location based on sensed forces [2]. However, this technique is hard to use in many robot manipulation problems because of secondary load paths.…”

Section: Introductionmentioning

confidence: 99%

Localization and manipulation of small parts using GelSight tactile sensing

Li¹,

Platt

Yuan³

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

216

178

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Abstract-Robust manipulation and insertion of small parts can be challenging because of the small tolerances typically involved. The key to robust control of these kinds of manipulation interactions is accurate tracking and control of the parts involved. Typically, this is accomplished using visual servoing or force-based control. However, these approaches have drawbacks. Instead, we propose a new approach that uses tactile sensing to accurately localize the pose of a part grasped in the robot hand. Using a feature-based matching technique in conjunction with a newly developed tactile sensing technology known as GelSight that has much higher resolution than competing methods, we synthesize high-resolution height maps of object surfaces. As a result of these high-resolution tactile maps, we are able to localize small parts held in a robot hand very accurately. We quantify localization accuracy in benchtop experiments and experimentally demonstrate the practicality of the approach in the context of a small parts insertion problem.

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“…Basic control tasks for humans, such as reaching and grasping, are often onerous for human operators of robot systems. Such robot control problems persist even for able bodied human operators given state-ofthe-art sensing and actuation platforms, such as the NASA Robonaut [1].…”

Section: Introductionmentioning

confidence: 99%

2D Subspaces for Sparse Control of High-DOF Robots

Jenkins

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-We investigate the use of five dimension reduction and manifold learning techniques to estimate a 2D subspace of hand poses for the purpose of generating motion. Our aim is to uncover a 2D parameterization from optical motion capture data that allows for transformation sparse user input trajectories into desired hand movements. The use of shape descriptors for representing hand pose is additionally explored for dealing with occluded parts of the hand during data collection. We present early results from uncovering 2D parameterizations of power and precision grasps and their use to drive a physically simulated hand from 2D mouse input.

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Building an autonomous humanoid tool user

Cited by 20 publications

References 15 publications

A survey of Tactile Human–Robot Interactions

A survey of Tactile Human–Robot Interactions

Localization and manipulation of small parts using GelSight tactile sensing

2D Subspaces for Sparse Control of High-DOF Robots

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