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Learning Anticipatory Control: A Trace for Intention Recognition

Abstract : Recent psychological experiments intend to show that social intentions can be read from the recording of motor actions (Becchio, Sartori, and Castiello 2010; Ferri et al. 2011). At the center of the debate is the hypothesis that the motor system is (Blackemore and Decety 2001), or is not (Jacob and Jeannerod 2005) used to recognize social intentions , with a potential openning to a bottom-up understanding of social behavior, agentivity and theory of mind. In (Becchio et al. 2007), the authors proposed to record the arm's trajectories during episodes of a "pick and place" task with a motor vs social outcome. The results provided evidence for differences in motor patterning depending on the social context and intention, but where not yet a direct evidence of the involvement of the motor system in recognizing social intention. In (Becchio, Sartori, and Castiello 2010; Ferri et al. 2011), the authors show how social affordances can change the movement parametrization with the hypothesis that a same action linked to a social context may involve an increase of the index of difficulty. Such experiments raise the issue of understanding an-ticipatory motor control and how the recognition of social situations affects at a very low level the generation of motor trajectories, and conversely, how trajectories, as a trace of intentions, can affect the social environement. In this paper, we present a pluridsciplinary 1 , study dedicated to understand the link between anticipatory motor control and motor intentions. Our goal is to propose a control architecture for a humanoid robot based on hydraulic technology (Fig. 1), with a potential of high degree of compliance. We have first conducted psychological experiments (Lewkowicz et al. 2013) designed to record the kinematic of the hand of human subjects during a two player's game (similar to the jungle speed game,Fig. 3) involving an object and 3 condition of play (moving the object 1) to play, 2) for "me", 3) for the other player). In these studies, we have confirmed that typical velocity profiles are affected by the subject's motor intentions according to the final destination of the object. Interestingly, we also developed in this study a simple feedforward NN
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Submitted on : Thursday, December 3, 2020 - 4:21:57 PM
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  • HAL Id : hal-01314017, version 1


Pierre Andry, David Bailly, Nils Beaussé, Arnaud Blanchard, D Lewkowicz, et al.. Learning Anticipatory Control: A Trace for Intention Recognition. AAAI Fall Symposium, Nov 2014, Washington, United States. ⟨hal-01314017⟩



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