Understanding haptics by evolving mechatronic systems

Loeb, Gerald E.; Tsianos, George A.; Fishel, Jeremy A.; Wettels, Nicholas; Schaal, Stefan

doi:10.1016/b978-0-444-53355-5.00009-9

Cited by 14 publications

(17 citation statements)

References 37 publications

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“…We have proposed that humans use a careful selection of exploratory movements to test hypotheses when exploring objects by touch (Loeb et al, 2011). Consider a simple example of identifying a brick by touch.…”

Section: Methodsmentioning

confidence: 99%

“…When interacting with an object, humans not only need to interpret the tactile information they sense, they also need to decide which types of movements to make in order to produce these tactual percepts. Artificial systems will require similar strategies (Loeb et al, 2011). Experimental psychologists have identified six general types of exploratory movements that humans make when tactually exploring objects to determine their properties: enclosure to determine global shape and volume, hefting to determine weight, pressure to determine hardness, static contact to determine thermal properties, contour following to determine exact shape, and lateral sliding movements to determine surface texture (Lederman and Klatzky, 1987).…”

Section: Introductionmentioning

confidence: 99%

“…However, as the complexity of discrimination tasks increase beyond simply texture discrimination and more movements and signals are added to the repertoire, the feasibility of doing everything (especially for multiple trials) can become highly impractical if not completely infeasible. Instead, the task of discrimination can take advantage of a hypothesis-testing approach that we have proposed humans likely use when exploring objects (Loeb et al, 2011). Each successive exploratory movement can be used to reduce the set of possible candidates; this information can be used to determine the optimal next exploratory movement that would yield the best discrimination among these most likely candidates rather than the entire population.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian Exploration for Intelligent Identification of Textures

Fishel¹,

Loeb²

2012

Front. Neurorobot.

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315

275

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In order to endow robots with human-like abilities to characterize and identify objects, they must be provided with tactile sensors and intelligent algorithms to select, control, and interpret data from useful exploratory movements. Humans make informed decisions on the sequence of exploratory movements that would yield the most information for the task, depending on what the object may be and prior knowledge of what to expect from possible exploratory movements. This study is focused on texture discrimination, a subset of a much larger group of exploratory movements and percepts that humans use to discriminate, characterize, and identify objects. Using a testbed equipped with a biologically inspired tactile sensor (the BioTac), we produced sliding movements similar to those that humans make when exploring textures. Measurement of tactile vibrations and reaction forces when exploring textures were used to extract measures of textural properties inspired from psychophysical literature (traction, roughness, and fineness). Different combinations of normal force and velocity were identified to be useful for each of these three properties. A total of 117 textures were explored with these three movements to create a database of prior experience to use for identifying these same textures in future encounters. When exploring a texture, the discrimination algorithm adaptively selects the optimal movement to make and property to measure based on previous experience to differentiate the texture from a set of plausible candidates, a process we call Bayesian exploration. Performance of 99.6% in correctly discriminating pairs of similar textures was found to exceed human capabilities. Absolute classification from the entire set of 117 textures generally required a small number of well-chosen exploratory movements (median = 5) and yielded a 95.4% success rate. The method of Bayesian exploration developed and tested in this paper may generalize well to other cognitive problems.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Exploration for Intelligent Identification of Textures

Fishel¹,

Loeb²

2012

Front. Neurorobot.

Self Cite

315

275

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show abstract

“…Haptic functions are difficult to study using the usual reductionist recording methods in trained subjects (Loeb et al, 2011). In addition to the usual problems of measuring both the neural activity and the kinematic details of these complex behaviors, it seems likely that the neural activity will depend on internal states that cannot be controlled or measured.…”

Section: What Can Robotics Contribute To Neuroscience?mentioning

confidence: 99%

Bayesian Action&Perception: Representing the World in the Brain

Loeb

Fishel

2014

Front. Neurosci.

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Theories of perception seek to explain how sensory data are processed to identify previously experienced objects, but they usually do not consider the decisions and effort that goes into acquiring the sensory data. Identification of objects according to their tactile properties requires active exploratory movements. The sensory data thereby obtained depend on the details of those movements, which human subjects change rapidly and seemingly capriciously. Bayesian Exploration is an algorithm that uses prior experience to decide which next exploratory movement should provide the most useful data to disambiguate the most likely possibilities. In previous studies, a simple robot equipped with a biomimetic tactile sensor and operated according to Bayesian Exploration performed in a manner similar to and actually better than humans on a texture identification task. Expanding on this, “Bayesian Action&Perception” refers to the construction and querying of an associative memory of previously experienced entities containing both sensory data and the motor programs that elicited them. We hypothesize that this memory can be queried (i) to identify useful next exploratory movements during identification of an unknown entity (“action for perception”) or (ii) to characterize whether an unknown entity is fit for purpose (“perception for action”) or (iii) to recall what actions might be feasible for a known entity (Gibsonian affordance). The biomimetic design of this mechatronic system may provide insights into the neuronal basis of biological action and perception.

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“…Moreover, the development of sophisticated anthropomorphic robotic hands requires knowledge from human haptic perception. Interestingly, the reverse is also true; robotic platforms can be used to develop and test theories of haptic perception because of their ability to reproduce movement and forces in a xxxx-xxxx/0x/$xx.00 © 200x IEEE T ----------------precise and repetitive manner [15,16].…”

Section: Introductionmentioning

confidence: 99%

Identifying Haptic Exploratory Procedures by Analyzing Hand Dynamics and Contact Force

Jansen¹,

Tiest²,

Kappers³

2013

IEEE Trans. Haptics

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Abstract-Haptic exploratory procedures (EPs) are prototypical hand movements that are linked to the acquisition of specific object properties. In studies of haptic perception, hand movements are often classified into these EPs. Here, we aim to investigate several EPs in a quantitative manner to understand how hand dynamics and contact forces differ between them. These dissimilarities are then used to construct an EP identification model capable of discriminating between EPs based on index finger position and contact force. The extent to which the instructed EPs were distinct, repeatable and similar across subjects was confirmed by showing that more that 95% of the analyzed trials were classified correctly. Finally, the method is employed to investigate haptic exploratory behavior during similarity judgments based on several object properties. It seems that discrimination based on material properties (hardness, roughness, and temperature) yields more consistent classification results compared to discrimination based on the acquisition of shape information.

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Understanding haptics by evolving mechatronic systems

Cited by 14 publications

References 37 publications

Bayesian Exploration for Intelligent Identification of Textures

Bayesian Exploration for Intelligent Identification of Textures

Bayesian Action&Perception: Representing the World in the Brain

Identifying Haptic Exploratory Procedures by Analyzing Hand Dynamics and Contact Force

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