Tactile Feedback Modes

The contact and pressure feedback provided by the voice-coil actuator activate the user’s FA-I and SA-I tactile afferents in a similar way that a directly manipulated object would activate these afferents. This feedback mode informs the user if either or both of the robotic fingers are contacting an object and gives the user an idea of the amount of force the robot is applying to the object. The system independently controls the force of the voice-coil actuator on the index finger and thumb based on the corresponding robot finger’s sensor reading. The robot’s symmetric hand and infinitely rotatable wrist make it necessary to assign the corresponding finger. I chose to assign the fingers based on the robot’s arm configuration so that the robot’s index finger is chosen to be the robotic finger that is closer in position to the human’s index finger. For example, when the gripper is facing directly outward, as in Fig. 4.1, the lateral finger is labeled as the index finger.

To calculate appropriate commanded forces to the voice coil, I first process the data from the index and thumb pressure sensor arrays in the same way as in [84]. I obtain one reading from the pressure cell arrays on the robot’s thumb and index finger by finding the total force applied to the 15 pressure cells on the finger’s flat gripping surface. Although this reading contains little noise, there is a noticeable drift caused by deformations in the rubber covering the pressure sensor arrays and other sensor

routine by setting the average of the first 0.25 seconds of data to zero. To determine whether or not the platform attached to the voice coil magnet should be contacting the finger, I compare the resulting pressure reading to 1 N, a level slightly higher than the drift observed in the sensors during typical interactions. When the pressure reading is below this level, the controller commands a current to the voice coil to keep the platform away from the user’s finger, as shown in the left picture of Fig. 4.4. If the pressure reading rises about this level, the controller commands the platform to contact the finger with a force proportional to the force at the robot’s finger, up to 6.7 N, as shown in the right picture of Fig. 4.4. In this version of the controller, I set this proportionality constant so that when the robot’s gripper is stalled while attempting to crush a rigid object, the voice coils output their maximum 6.7 N. In the future, rigorous testing of this feedback mode will help refine this proportionality constant. Finally, I note that although the platform attached to the voice coil’s magnet applies a force to the user’s finger, this force is matched by an equal and opposite reaction force applied to the back of the finger. Since these internal forces negate each other, the fingertip contact and pressure feedback does not greatly influence the dynamics or stability of the system.

Acceleration Feedback

Acceleration feedback was used to stimulate the user’s FA-II afferents by playing pro- cessed accelerations measured by the PR2’s accelerometer through the voice coils;

these signals naturally convey important information about contact events in the robot’s environment. Drawing heavily on previous work conducted in the Penn Hap- tics Lab, I digitally process the accelerometer data to obtain clear signals that can readily reveal important contact events. First, the three axes of acceleration data are summed to obtain a single accelerometer reading, a computationally efficient method that introduces no time delay while still providing a good temporal and spectral match with the original three-axis signal [57]. The resulting acceleration signal is filtered using a fourth order 150 to 750 Hz Butterworth bandpass filter to remove the low frequency gravity component and a strong signal at 1000 Hz. The filtered signal contains both accelerations caused by contact events and accelerations caused by the motors and cooling of the PR2. To isolate the important contact accelerations from the ego-vibrations of the robot, I implemented an adaptive spectral subtraction method, similar to the method described in [65]. In adaptive spectral subtraction, short segments of the time domain acceleration signal are transformed to the fre- quency domain, where a continually updated estimate of the robot’s ego-vibration spectrum is subtracted from the total spectrum of the signal. The remaining signal content, which contains the spectrum of contact events, is then converted back to the time domain. The resulting processed acceleration signal is then scaled to command appropriate levels of current to the voice coil.

I sought to scale the vibration feedback so that a processed vibration signal con- taining only robot ego-vibrations would be barely perceptible by the user, allowing the

contact acceleration transients to be most salient. I found two main factors that each independently affect the strength of the vibration feedback. First, the acceleration feedback feels drastically different depending on whether or not the voice coil’s mag- net is being attracted to or repelled from the coil. When the magnet is being attracted to the coil, the vibrating magnet is in direct contact with the neoprene foam, and the vibrations are transmitted throughout the device. Somewhat surprisingly, this creates stronger vibrotactile feedback than when the magnet is being repelled from the coil and the platform is in direct contact with the fingertip. Second, although the adaptive spectral subtraction removes much of the robot’s own vibrations, a dis- cernible acceleration signal caused by the opening and closing of the PR2’s gripper is not eliminated, as seen in Fig. 4.6; this sustained vibration is unpleasant to feel. For these reasons, a different scale factor is used for each of the four combinations (at- traction or repulsion between the coil and the magnet and movement or stationarity of the robotic gripper). The acceleration gains for the finger and thumb are switched independently.