Humans can feel, weigh and grasp diverse objects, and simultaneously infer their material properties while applying the right amount of force—a challenging set of tasks for a modern robot1. Mechanoreceptor networks that provide sensory feedback and enable the dexterity of the human grasp2 remain difficult to replicate in robots. Whereas computer-vision-based robot grasping strategies3,4,5 have progressed substantially with the abundance of visual data and emerging machine-learning tools, there are as yet no equivalent sensing platforms and large-scale datasets with which to probe the use of the tactile information that humans rely on when grasping objects. Studying the mechanics of how humans grasp objects will complement vision-based robotic object handling. Importantly, the inability to record and analyse tactile signals currently limits our understanding of the role of tactile information in the human grasp itself—for example, how tactile maps are used to identify objects and infer their properties is unknown6. Here we use a scalable tactile glove and deep convolutional neural networks to show that sensors uniformly distributed over the hand can be used to identify individual objects, estimate their weight and explore the typical tactile patterns that emerge while grasping objects. The sensor array (548 sensors) is assembled on a knitted glove, and consists of a piezoresistive film connected by a network of conductive thread electrodes that are passively probed. Using a low-cost (about US$10) scalable tactile glove sensor array, we record a large-scale tactile dataset with 135,000 frames, each covering the full hand, while interacting with 26 different objects. This set of interactions with different objects reveals the key correspondences between different regions of a human hand while it is manipulating objects. Insights from the tactile signatures of the human grasp—through the lens of an artificial analogue of the natural mechanoreceptor network—can thus aid the future design of prosthetics7, robot grasping tools and human–robot interactions1,8,9,10.
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Custom code used in the current study is available from the corresponding author on request.
Source data for key figures in the manuscript are included as interactive maps in Supplementary Data 1–3. Please load (and refresh) all ‘∗.html’ pages in Firefox or Chrome. The tactile datasets generated and analysed during this study are available from the corresponding author on request.
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S.S. thanks M. Baldo, V. Bulovic and J. Lang for their comments and discussions. P.K. and S.S. thank K. Myszkowski for discussions. We gratefully acknowledge support from the Toyota Research Institute.
Nature thanks Giulia Pasquale and Alexander Schmitz for their contribution to the peer review of this work.
The authors declare no competing interests.
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Extended data figures and tables
a, Image of the finished STAG just before the electrodes are insulated. b, Scan of the STAG. c, Electrical-grounding-based signal isolation circuit (based on ref. 22). The active row during readout is selected by grounding one of the 32 single-pole double throw (SPDT) switches. A 32:1 analog switch is used to select one of the 32 columns at a time. Here Rc is the charging resistor, Vref is the reference voltage, and Rg sets the amplifier gain. d, Fabricated printed circuit board that interfaces with the STAG. The two connectors shown on the top right and bottom are connected to the column and row electrodes of the sensor matrix. The charging resistors (Rc) are on the back of the printed circuit board.
a, The resistance of a single sensing element shows the linear working range (in logarithmic force units). The sensor is not sensitive below about 20 mN of force and saturates in response when a load exceeding 0.8 N is applied. b, Response of three separate sensors in the force range 20 mN to 0.5 N. The sensors show minimal hysteresis (17.5 ± 2.8%; see Supplementary Fig. 2). c, The sensor response after 10, 100 and 1,000 cycles of linear force ramps up to 0.5 N for three separate devices. The resistance measurements are shown in d over the entire set of cycles. e, Differential scanning calorimetry measurements of the FSF material shows a two-polymer blend response with softening/melting temperatures of around 100 °C and 115.1 °C. f, Through-film resistance of an unloaded sensor after treating at different temperatures in a convection oven for 10 min. The film becomes insulating above about 80 °C.
a, A simplified version of the sensor laminate architecture. b, The sensor is assembled by laminating a FSF along with orthogonal electrodes on each side, that are held in place and insulated by a layer of two-sided adhesive and a stretchable LDPE film (see Methods). c, Fixture used to assemble parallel electrodes. The individual electrodes can be threaded into the structure (like a needle) for assembling parallel electrodes with a spacing of 2.5 mm. d, Assembled version of the architecture shown in a. e, A regular 32 × 32 array version of the STAG based on the design in b.
Extended Data Figure 4 Sample recordings of nine objects on regular 32 × 32 arrays on a flat surface.
Nine different objects are manipulated on a regular sensor array (Extended Data Fig. 3d) placed on a flat surface. The resting patterns of these objects can be seen easily. Pressing the tactile array with sharp objects like a pen or the needles of a kiwano yields signals with a single sensor resolution.
a, Standard auxetic design laser cut from the FSF. b, The actual design of the auxetic includes holes to route the electrodes (shown in red and blue), and slots allow the square, sensing island to rotate, enhancing the stretchability of the sensor array. c, Close-up of the fabricated array showing the conductive thread electrodes before insulation. d, A fully fabricated 10 × 10 array with an auxetic design. e, Auxetic patterning allows the sensor array to be folded, crushed and stretched easily with no damage. f, The array can also be stretched in multiple directions (see Supplementary Video 2).
In total, 26 objects are used in our dataset; images of 24 objects are shown here. In addition to these objects, our dataset includes two cola cans (one empty can and one full can).
a–h, The actual object and predicted object labels are shown in these confusion matrices for different networks, each taking 1 to 8 (or N) inputs where each input is obtained from a distinct cluster for N > 1 (approach shown in Fig. 2e; see Methods). These matrices correspond to the ‘clustering’ curve in Fig. 2b. Objects with similar shapes, sizes or weights are more likely to be confused with each other. For example, the empty can and full can are easily mistaken for each other when they are resting on the table. Likewise, lighter objects such as the safety glasses, plastic spoon, or the coin are more likely to be confused with each other or other objects. Large, heavy objects with distinct signatures such as the tea box have high detection accuracy across different numbers of inputs (N). i, Original first-layer convolution filters (3 × 3) learned by the network shown in Fig. 2a for N = 1 inputs. j, Visualization of the first-layer convolution filters of ResNet-18 trained on ImageNet.
a, Four representative examples from the weight estimation dataset, in which the objects are lifted using multi-finger grasps from the top (see Supplementary Video 6 for an example recording). b, The weight estimation performance is shown in terms of the mean absolute and relative errors (normalized to the weight of each object) in each weight interval. The relative error is analogous to the Weber fraction. We observe that the CNN outperforms the linear baseline with or without the hand pose signal removed. The overall errors of the two linear baselines are comparable.
Extended Data Figure 9 Correspondence maps for six individual sensors using the decomposed hand pose signal.
The hand pose signal decomposed from object interactions is used to collectively extract correlations between the sensors and the full hand (analogous to Fig. 3b where the decomposed object-related signal is used). The pixels at the fingertips show less structured correlations with the remaining fingers, unlike in Fig. 3b.
a, Images of the hand poses used in the hand pose dataset. The poses G1 to G7 are extracted from a recent grasp taxonomy. In the recordings, each pose is continuously articulated from the neutral empty hand pose. b, When the tactile data from this dataset is clustered using t-SNE, each distinct group represents a hand pose. Sample tactile maps are shown on the right. The corresponding samples are marked in red (see Supplementary Data 3). c, The hand pose signals can be classified with 89.4% accuracy (average of ten runs with 3,080 training frames and 1,256 distinct test frames) using the same CNN architecture shown in Fig. 2a. The confusion matrix elements denote how often each hand pose (column) is classified as one of the possible hand poses (rows). It shows that hand poses G1 and G6 are sometimes misidentified but the other hand poses are identified nearly perfectly.
This file contains Supplementary Table 1, Supplementary References and Supplementary Figures 1-6
This zipped file contains the interactive version of the k-means clustering example in Fig. 2e
This zipped file contains ‘sensor-level.html’, ‘region-level.html’ and ‘finger-level.html’ which contain interactive maps of the correlations seen over the entire dataset – between the individual sensors, different hand regions, and fingers respectively. ‘sensor-level.html’ is the interactive version of the map in Fig. 3b
This zipped file contains an interactive map of the t-SNE clustered hand pose data shown in Extended Data Fig. 10b
Video shows the bendability of the STAG and includes a demonstration of folding a paper plane while wearing the STAG
Auxetic version of the sensor array with 10 × 10 elements (speed – 3x)
Interaction from the STAG dataset – Mug (speed – 3x)
Interaction from the STAG dataset – Cat [stone] (speed – 3x)
Interaction from the STAG dataset – Safety glasses (speed – 3x)
Example sequence of the dataset used for weight estimation – Multimeter (speed – 3x)
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Sundaram, S., Kellnhofer, P., Li, Y. et al. Learning the signatures of the human grasp using a scalable tactile glove. Nature 569, 698–702 (2019). https://doi.org/10.1038/s41586-019-1234-z
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