Guy Avraham

Toward Perceiving Robots as Humans: Three Handshake Models Face the Turing-Like Handshake Test

In the Turing test a computer model is deemed to “think intelligently” if it can generate answers that are indistinguishable from those of a human. We developed an analogous Turing-like handshake test to determine if a machine can produce similarly indistinguishable movements. The test is administered through a telerobotic system in which an interrogator holds a robotic stylus and interacts with another party - artificial or human with varying levels of noise. The interrogator is asked which party seems to be more human. Here, we compare the human-likeness levels of three different models for handshake: (1) Tit-for-Tat model, (2) λ model, and (3) Machine Learning model. The Tit-for-Tat and the Machine Learning models generated handshakes that were perceived as the most human-like among the three models that were tested. Combining the best aspects of each of the three models into a single robotic handshake algorithm might allow us to advance our understanding of the way the nervous system controls sensorimotor interactions and further improve the human-likeness of robotic handshakes.

Representing delayed force feedback as a combination of current and delayed states

To adapt to deterministic force perturbations that depend on the current state of the hand, internal representations are formed to capture the relationships between forces experienced and motion. However, information from multiple modalities travels at different rates, resulting in intermodal delays that require compensation for these internal representations to develop. To understand how these delays are represented by the brain, we presented participants with delayed velocity-dependent force fields, i.e., forces that depend on hand velocity either 70 or 100 ms beforehand. We probed the internal representation of these delayed forces by examining the forces the participants applied to cope with the perturbations. The findings showed that for both delayed forces, the best model of internal representation consisted of a delayed velocity and current position and velocity. We show that participants relied initially on the current state, but with adaptation, the contribution of the delayed representation to adaptation increased. After adaptation, when the participants were asked to make movements with a higher velocity for which they had not previously experienced with the delayed force field, they applied forces that were consistent with current position and velocity as well as delayed velocity representations. This suggests that the sensorimotor system represents delayed force feedback using current and delayed state information and that it uses this representation when generalizing to faster movements.NEW & NOTEWORTHY The brain compensates for forces in the body and the environment to control movements, but it is unclear how it does so given the inherent delays in information transmission and processing. We examined how participants cope with delayed forces that depend on their arm velocity 70 or 100 ms beforehand. After adaptation, participants applied opposing forces that revealed a partially correct representation of the perturbation using the current and the delayed information.

Three alternatives to measure the human-likeness of a handshake model in a turing-like test

In the Turing test, a computer model is deemed to think intelligently if it can generate answers indistinguishable from those of a human. We proposed a Turing-like handshake test for testing motor aspects of machine intelligence. The test is administered through a telerobotic system in which an interrogator holds a robotic stylus and interacts with another party—human, artificial, or a linear combination of the two. Here, we analyze and test experimentally the properties of three versions of the Turing-like handshake test: Pure, Weighted, and Noise. We follow the framework of signal detection theory, and propose a simplified model for the interrogator human-likeness evaluation; we simulate this model and provide an assessment of the statistical power of each version of the handshake test. Our simulation study suggests that the choice of the best test depends on how well the interrogator identifies a human handshake when compared with a model. The Pure test is better for intermediate and large levels of interrogator confusion, and the Weighted and Noise tests are good for low levels of confusion. We then present the results of an experimental study in which we compare among three simple models for a human handshake. We demonstrate that it is possible to distinguish between these handshake models, and discuss the relative advantage of each measure and future possible handshake models and Turing-like tests, in measuring and promoting the design of human-like robots for robotics rehabilitation, teleoperation, and telepresence.

State-Based Delay Representation and Its Transfer from a Game of Pong to Reaching and Tracking

Abstract To accurately estimate the state of the body, the nervous system needs to account for delays between signals from different sensory modalities. To investigate how such delays may be represented in the sensorimotor system, we asked human participants to play a virtual pong game in which the movement of the virtual paddle was delayed with respect to their hand movement. We tested the representation of this new mapping between the hand and the delayed paddle by examining transfer of adaptation to blind reaching and blind tracking tasks. These blind tasks enabled to capture the representation in feedforward mechanisms of movement control. A Time Representation of the delay is an estimation of the actual time lag between hand and paddle movements. A State Representation is a representation of delay using current state variables: the distance between the paddle and the ball originating from the delay may be considered as a spatial shift; the low sensitivity in the response of the paddle may be interpreted as a minifying gain; and the lag may be attributed to a mechanical resistance that influences paddle’s movement. We found that the effects of prolonged exposure to the delayed feedback transferred to blind reaching and tracking tasks and caused participants to exhibit hypermetric movements. These results, together with simulations of our representation models, suggest that delay is not represented based on time, but rather as a spatial gain change in visuomotor mapping.

Neglect-Like Effects on Drawing Symmetry Induced by Adaptation to a Laterally Asymmetric Visuomotor Delay

In daily interactions, our sensorimotor system accounts for spatial and temporal discrepancies between the senses. Functional lateralization between hemispheres causes differences in attention and in the control of action across the left and right workspaces. In addition, differences in transmission delays between modalities affect movement control and internal representations. Studies on motor impairments such as hemispatial neglect syndrome suggested a link between lateral spatial biases and temporal processing. To understand this link, we computationally modeled and experimentally validated the effect of laterally asymmetric delay in visual feedback on motor learning and its transfer to the control of drawing movements without visual feedback. In the behavioral experiments, we asked healthy participants to perform lateral reaching movements while adapting to delayed visual feedback in either left, right, or both workspaces. We found that the adaptation transferred to blind drawing and caused movement elongation, which is consistent with a state representation of the delay. However, the pattern of the spatial effect varied between conditions: whereas adaptation to delay in only the left workspace or in the whole workspace caused selective leftward elongation, adaptation to delay in only the right workspace caused drawing elongation in both directions. We simulated arm movements according to different models of perceptual and motor spatial asymmetry in the representation of delay and found that the best model that accounts for our results combines both perceptual and motor asymmetry between the hemispheres. These results provide direct evidence for an asymmetrical processing of delayed visual feedback that is associated with both perceptual and motor biases that are similar to those observed in hemispatial neglect syndrome.

Adaptation to a Space-variant Visuomotor Delay can Cause Neglect-like Effects on Drawing Symmetry

In daily interactions, our sensorimotor system accounts for spatial and temporal discrepancies between the senses. Functional lateralization between hemispheres causes differences in attention and control of action. In addition, differences in transmission delays between modalities affects motor control. Studies on hemispatial neglect syndrome suggest a link between temporal processing and lateral spatial biases. To understand this link, we studied participants who performed lateral reaching, and adapted to delayed visual feedback in either left, right, or both workspaces. We tested transfer of adaptation to blind drawing, and found that adaptation to left or both delay caused selective leftward elongation. In contrast, adaptation to right delay caused elongation in both directions. Arm dynamics alone cannot explain these findings, but a model of a combined attentional-motor asymmetry across the hemispheres explains our observations. This suggests a possible connection between laterality in delay processing and motor performances observed in cases of hemispatial neglect.