Figure 1.
Flowchart for an iterative process for designing a robot model. This is a modified version of the chart from Lewis and Cañamero (2017), which is based on, and closely follows, the process described in van der Staay (2006) and van der Staay et al. (2009). Numbers in circles are to facilitate references to individual steps in the text. Note that, even after the robot model is accepted for clinical use (Stage 10), it is envisioned that robot model development might continue iteratively and that incremental improvements will be made with each loop through the process.
Figure 2.
An overview of the action selection mechanism for our robot. Rounded boxes represent individual (potentially nested) behaviors, while square-cornered boxes represent other internal components. The actions of the actuators result in changes in the environment and the robot’s physiology, which is fed back to the robot controller via the robot’s perceptions. Motivations are updated and new behaviors are selected every action selection loop (10 Hz).
Table 1.
The robot’s physiological variables
| Variable | Fatal limit | Ideal value | Maintenance |
|---|---|---|---|
| Energy | 0 | 1,000 | decreases over time; increases when the robot consumes from an energy resource |
| Integrity | 0 | 1,000 | decreases on contact with objects; increases over time as the robot “heals” |
| Integument L | none | 1,000 | decreases over time; increases when the robot’s left side passes close to a grooming post |
| Integument R | none | 1,000 | decreases over time; increases when the robot’s right side passes close to a grooming post |
Figure 3.
The Elisa-3 robot. Left: an Elisa-3 robot, viewed from the front/left. Right: a diagram of the Elisa-3’s infrared distance sensors (top view). Arrows indicate how the sensors are used to detect grooming and damage from collisions and sustained rubbing.
Figure 4.
The 80 cm × 80 cm environment used in the experiment. Here the robot is feeding at an energy resource (white patch) while the grooming posts (white pipes) stand on the black patches.
Table 2.
Experimental results
| Condition | No. of deaths | Mean arithmetic well-being | Mean geometric well-being | Mean variance | Percentage time grooming | Percentage time eating | Percentage time with zero integument |
|---|---|---|---|---|---|---|---|
| 1 | 2/20 | 560.9 | 456.5 | 55,438 | 34.5 | 21.3 | 13.8 |
| 2 | 3/20 | 603.2 | 501.1 | 57,435 | 39.4 | 20.6 | 12.5 |
| 3 | 19/20 | 556.5 | 344.9 | 101,264 | 64.9 | 13.0 | 27.0 |
[i] Note. The mean well-beings and variance have been calculated by taking the means over the lifetime for each “robot” (run) and then calculating the mean of the 20 values in each condition. The percentages in the last three columns have been calculated by concatenating the lifetimes of the robots in the 20 runs in each condition and calculating what percentage of this time was spent grooming, and so on.
Figure 5.
Experimental results: the means of the robot’s geometric well-being over the lifetime of each run. Larger values indicate better maintained physiological variables. Crosses indicate runs in which the robot died.
Figure 6.
Experimental results: the means of the variance of the robot’s physiological variables (which can be thought of as a measure of the robot’s “physiological balance”) over the lifetime of each run. Smaller values indicate better balance between the different physiological variables. Crosses indicate runs in which the robot died.
Figure 7.
Experimental results: percentage of the robot’s lifetime during which the physiological variable (PV) closest to the critical limit of zero was in four “regions” of the physiological space. With a value of exactly 0 (the variable with a value of zero here must be an integument variable, since if it had been one of the survival-related variables, the robot would have been dead), in the range (0, 100] (intuitively “highly critical”), in the range (100, 200] (“critical”), and in the range (200, 300] (“danger”). These percentages were calculated by concatenating the lifetimes of the robots in the 20 runs for each condition and calculating the percentage of this time during which the physiological variable that was closest to the critical limit was in each region. The equal zero percentages correspond to the values in Table 2, last column.
Figure 8.
Experimental results: the percentage of the robot’s lifetime that either of the two integument variables was the largest valued (i.e., most well maintained) physiological variable (PV). Crosses indicate runs in which the robot died.
Figure 9.
Experimental results: the percentage of the robot’s lifetime that either of the two integument variables was the smallest valued (i.e., least well maintained) physiological variable (PV). Crosses indicate runs in which the robot died.