Data-driven thermal recognition of contact with people and objects

Bhattacharjee, Tapomayukh; Wade, Joshua; Chitalia, Yash; Kemp, Charles C.

doi:10.1109/haptics.2016.7463193

Cited by 16 publications

(17 citation statements)

References 34 publications

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“…Prior works have used data-driven models to train robots how to classify common materials from thermal sensors [13], [18], [34], [35]. Kerr et al [18] used data-driven methods to train a robot to recognize materials using thermal information from the BioTac sensor, and found that the robot outperformed human participants.…”

Section: B Robot Thermal Perceptionmentioning

confidence: 99%

Material Recognition via Heat Transfer Given Ambiguous Initial Conditions

Bhattacharjee

Clever

Wade

et al. 2021

IEEE Trans. Haptics

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Humans and robots can recognize materials with distinct thermal effusivities by making physical contact and observing temperatures during heat transfer. This works well with room temperature materials, yet research has shown that contact with distinct materials can result in similar temperatures and confusion when one material is heated or cooled. To thoroughly investigate this form of ambiguity, we designed a psychophysical experiment in which a participant discriminates between two materials given initial conditions that result in similar temperatures (i.e., ambiguous initial conditions). We conducted a study with 32 human participants and a robot. Humans and the robot confused the materials. We also found that robots can overcome this ambiguity using two temperature sensors with different temperatures prior to contact. We support this conclusion based on a mathematical proof using a heat transfer model and empirical results in which a robot achieved 100% accuracy compared to 5% human accuracy. Our results also indicate that robots with a single temperature sensor can use subtle cues to outperform humans. Overall, our work provides insights into challenging conditions for material recognition via heat transfer, and suggests methods by which robots can overcome these challenges.

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Section: B Robot Thermal Perceptionmentioning

confidence: 99%

Material Recognition via Heat Transfer Given Ambiguous Initial Conditions

Bhattacharjee

Clever

Wade

et al. 2021

IEEE Trans. Haptics

Self Cite

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“…Despite not being as effective as the one stage ANN method presented in (Kerr et al, 2014a), this two stage approach obtained a classification rate of 83.81% for material group identification and 70.48% for individual material identification. Bhattacharjee et al (2016) continued their earlier research and presented a method for distinguishing between contact with inanimate objects and humans, using a novel portable handheld device developed by the authors consisting of three tactile sensing modalities: a force sensor to detect contact, a heat-transfer sensor that is actively heated, and a small thermally-isolated temperature sensor . Data was collected from the arms of 10 human subjects from 3 different locations on the right arm and 80 objects consisting of 8 similar objects from 10 different bathrooms (Bhattacharjee et al, 2016).…”

Section: Background and Related Researchmentioning

confidence: 95%

“…Bhattacharjee et al (2016) continued their earlier research and presented a method for distinguishing between contact with inanimate objects and humans, using a novel portable handheld device developed by the authors consisting of three tactile sensing modalities: a force sensor to detect contact, a heat-transfer sensor that is actively heated, and a small thermally-isolated temperature sensor . Data was collected from the arms of 10 human subjects from 3 different locations on the right arm and 80 objects consisting of 8 similar objects from 10 different bathrooms (Bhattacharjee et al, 2016). The effect of varying durations of contact made with the human subjects and the objects was also evaluated.…”

Section: Background and Related Researchmentioning

confidence: 95%

Material recognition using tactile sensing

Kerr

McGinnity

Coleman

2018

Expert Systems with Applications

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Identification of the material from which an object is made is of significant value for effective robotic grasping and manipulation. Characteristics of the material can be retrieved using different sensory modalities: vision based, tactile based or sound based. Compressibility, surface texture and thermal properties can each be retrieved from physical contact with an object using tactile sensors. This paper presents a method for collecting data using a biomimetic fingertip in contact with various materials and then using these data to classify the materials both individually and into groups of their type. Following acquisition of data, principal component analysis (PCA) is used to extract features. These features are used to train seven different classifiers and hybrid structures of these classifiers for comparison. For all materials, the artificial systems were evaluated against each other, compared with human performance and were all found to outperform human participants' average performance. These results highlighted the sensitive nature of the BioTAC sensors and pave the way for research that requires a sensitive and accurate approach such as vital signs monitoring using robotic systems.

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“…Some studies also used the BioTAC TM 's signal with machine learning such as Artificial Neural Network (ANN) [17] and Hidden Markov Models (HMMs) [18]. Rapid recognition estimation using learning technique is proposed in [19], extended it to the binary classification in real life [20], and developed the multimodal sensors [21].…”

Section: Introductionmentioning

confidence: 99%

Material Classification Using Active Temperature Controllable Robotic Gripper

Osawa

Kase

Domae

et al. 2021

Preprint

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Recognition techniques allow robots to make proper planning and control strategies to manipulate various objects. Object recognition is more reliable when made by combining several percepts, e.g., vision and haptics. One of the distinguishing features of each object's material is its heat properties, and classification can exploit heat transfer, similarly to human thermal sensation. Thermal-based recognition has the advantage of obtaining contact surface information in realtime by simply capturing temperature change using a tiny and cheap sensor. However, heat transfer between a robot surface and a contact object is strongly affected by the initial temperature and environmental conditions. A given object's material cannot be recognized when its temperature is the same as the robotic grippertip. We present a material classification system using active temperature controllable robotic gripper to induce heat flow. Subsequently, our system can recognize materials independently from their ambient temperature. The robotic gripper surface can be regulated to any temperature that differentiates it from the touched object's surface. We conducted some experiments by integrating the temperature control system with the Academic SCARA Robot, classifying them based on a long short-term memory (LSTM) using temperature data obtained from grasping target objects.

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Data-driven thermal recognition of contact with people and objects

Cited by 16 publications

References 34 publications

Material Recognition via Heat Transfer Given Ambiguous Initial Conditions

Material Recognition via Heat Transfer Given Ambiguous Initial Conditions

Material recognition using tactile sensing

Material Classification Using Active Temperature Controllable Robotic Gripper

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