Fig 5 - uploaded by Ali Shafti
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Left: Correlations between the behaviour of all participanttrained agents, as well as the models they tested against, S1, S5, S7 and expert. Right: Spatial representation of trained agents' behaviour correlation with that of the pre-model, for participants P2, P3, P6 and P7. The goal is marked as a green circle -see Figure 2 for reference. A higher correlation in a given position means that the final agent's policy has changed less from the original pre-model on which training started.

Left: Correlations between the behaviour of all participanttrained agents, as well as the models they tested against, S1, S5, S7 and expert. Right: Spatial representation of trained agents' behaviour correlation with that of the pre-model, for participants P2, P3, P6 and P7. The goal is marked as a green circle -see Figure 2 for reference. A higher correlation in a given position means that the final agent's policy has changed less from the original pre-model on which training started.

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The intuitive collaboration of humans and intelligent robots (embodied AI) in the real-world is an essential objective for many desirable applications of robotics. Whilst there is much research regarding explicit communication, we focus on how humans and robots interact implicitly, on motor adaptation level. We present a real-world setup of a human...

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Context 1
... this we check correlations between the different agents' action outputs when fed the same iterations of states as inputs. The result of this can be seen in Figure 5-Left. Note that, in between the participants, the expert agent has the highest correlations with those of P1, P2 and P3 -same participants that have the best performance with it. ...
Context 2
... and P3 show generally good performance on their own models, the expert model and S7. P6 and P7 have poor performance overall, though P7 plays well with S7. Figure 5Right, shows how these four participants' agents, developed their policies from the pre-model, in a spatial sense. The figure depicts the game tray, with the heatmap values re- flecting the correlation of the participant's agent's behaviour in each position, with that of the pre-model on which they started the training. ...
Context 3
... this we check correlations between the different agents' action outputs when fed the same iterations of states as inputs. The result of this can be seen in Figure 5-Left. Note that, in between the participants, the expert agent has the highest correlations with those of P1, P2 and P3 -same participants that have the best performance with it. ...
Context 4
... and P3 show generally good performance on their own models, the expert model and S7. P6 and P7 have poor performance overall, though P7 plays well with S7. Figure 5Right, shows how these four participants' agents, developed their policies from the pre-model, in a spatial sense. The figure depicts the game tray, with the heatmap values re- flecting the correlation of the participant's agent's behaviour in each position, with that of the pre-model on which they started the training. ...

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