Solving the Correspondence Problem Between Dissimilarly Embodied Robotic Arms Using the ALICE Imitation Mechanism

Imitation is a powerful mechanism whereby knowledge may be transferred between agents (both biological and artificial). A crucial problem in imitation is the correspondence problem, mapping action sequences of the model and the imitator agent. This problem becomes particularly obvious when the two agents do not share the same embodiment and affordances. This paper describes work with our general imitation mechanism called ALICE (Action Learning for Imitation via Correspondence between Embodiments) that specifically addresses the correspondence problem. The mechanism has been implemented in two different software test-beds. The previous implementation, chessworld, is briefly summarised and the current robotic arm manipulator implementation is presented in this paper. Using the robotic arm test-bed we present proof of concept for the social transmission of behavioural patterns through groups of heterogeneous agents. We also present experiments that illustrate the impact of synchronization, loose perceptual matching and proprioception on the imitative performance. The robustness and adaptive nature of the ALICE mechanism is further illustrated with examples where the imitator agent embodiment is changing during the initial and later stages of the learning process. 1 Agent-based perspective Imitation is a powerful learning mechanism and a general agent-based approach must be used in order to identify the most interesting and significant problems, rather than the prominent ad hoc approaches in imitation robotics research so far. The traditional approach concentrates in finding an appropriate mechanism for imitation and developing a robot control architecture that identifies salient features in the movements of an (often visually observed) model, and maps them appropriately (via a built-in and usually static method) to motor outputs of the imitator (Kuniyoshi et al. 1990, 1994). Model and imitator are usually not interacting with each other, neither do they share and perceive a common context. Effectively this kind of approach limits itself to answering the question of how to imitate for a particular robotic system and its particular imitation task. This has led to many diverse approaches to robot controllers for imitative learning that are difficult to generalize across different contexts and to different robot platforms. In contrast to the above, the agent-based approach for imitation considers the behaviour of an autonomous agent in relation to its environment, including other autonomous agents. The mechanisms underlying imitation are not divorced from the behaviour-incontext, including the social and non-social environments, motivations, relationships among the agents, the agent’s individual and learning history etc. (Dautenhahn and Nehaniv, 2002). Such a perspective helps unfold the full potential of research on imitation and helps in identifying challenging and important research issues. The agentbased perspective has a broader view and includes five central questions in designing experiments on research on imitation: who to imitate, when to imitate, what to imitate, how to imitate and how to evaluate a successful imitation. A systematic investigation of these research questions can show the full potential of imitation from an agent-based perspective. In addition to deciding who, when and what to imitate, an agent must employ the appropriate mechanisms to learn and carry out the necessary imitative actions. The embodiment of the agent and its affordances will play a crucial role, as stated in the correspondence problem (Nehaniv and Dautenhahn, 2002): Given an observed behaviour of the model, which from a given starting state leads the model through a sequence (or hierarchy) of sub-goals in states, action and/or effects, one must find and execute a sequence of actions using one’s own (possibly dissimilar) embodiment, which from a corresponding starting state, leads through corresponding sub-goals in corresponding states, actions, and/or effects, while possibly responding to corresponding events. This informal statement of the correspondence problem draws attention to the fact that the agents may not necessarily share the same morphology or may not share access to the same affordances even among members of the same “species”. This is true for both biological agents (e.g. differences in height among humans) and artificial agents (e.g. differences in motor and actuator properties). Having similar embodiments and/or affordances is just a special case of the more general problem. In order to study the correspondence problem we developed the ALICE (Action Learning via Imitation between Corresponding Embodiments) generic imitation mechanism, and implemented it in different simple test-beds. These test-beds were implemented using the Swarm agent simulation system