The identification of learning mechanisms for locomotion has been the subject of much research for some time but many challenges remain. Dynamic systems theory (DST) offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL) has offered a promising method to adaptively link the dynamic system to the environment it interacts with via a reward-based value system. In this paper, we propose a model that integrates the above perspectives and applies it to the case of a humanoid (NAO) robot learning to walk the ability of which emerges from its value-based interaction with the environment. In the model, a simplified central pattern generator (CPG) architecture inspired by neuroscientific research and DST is integrated with an actor-critic approach to RL (cpg-actor-critic). In the cpg-actor-critic architecture, least-square-temporal-difference based learning converges to the optimal solution quickly by using natural gradient learning and balancing exploration and exploitation. Futhermore, rather than using a traditional (designer-specified) reward it uses a dynamic value function as a stability indicator that adapts to the environment. The results obtained are analyzed using a novel DST-based embodied cognition approach. Learning to walk, from this perspective, is a process of integrating levels of sensorimotor activity and value.

During development, animals can spontaneously discover action-outcome pairings enabling subsequent achievement of their goals. We present a biologically plausible embodied model addressing key aspects of this process. The biomimetic model core comprises the basal ganglia and its loops through cortex and thalamus. We incorporate reinforcement learning (RL) with phasic dopamine supplying a sensory prediction error, signalling “surprising” outcomes. Phasic dopamine is used in a cortico-striatal learning rule which is consistent with recent data. We also hypothesized that objects associated with surprising outcomes acquire “novelty salience” contingent on the predicability of the outcome. To test this idea we used a simple model of prediction governing the dynamics of novelty salience and phasic dopamine. The task of the virtual robotic agent mimicked an in vivo counterpart (Gancarz et al., 2011) and involved interaction with a target object which caused a light flash, or a control object which did not. Learning took place according to two schedules. In one, the phasic outcome was delivered after interaction with the target in an unpredictable way which emulated the in vivo protocol. Without novelty salience, the model was unable to account for the experimental data. In the other schedule, the phasic outcome was reliably delivered and the agent showed a rapid increase in the number of interactions with the target which then decreased over subsequent sessions. We argue this is precisely the kind of change in behavior required to repeatedly present representations of context, action and outcome, to neural networks responsible for learning action-outcome contingency. The model also showed cortico-striatal plasticity consistent with learning a new action in basal ganglia. We conclude that action learning is underpinned by a complex interplay of plasticity and stimulus salience, and that our model contains many of the elements for biological action discovery to take place.

The vertebrate neuromodulatory systems are critical for appropriate value-laden responses to environmental challenges. Whereas changes in the overall level of dopamine (DA) have an effect on the organism's reward or curiosity-seeking behavior, changes in the level of serotonin (5-HT) can affect its level of anxiety or harm aversion. Moreover, top-down signals from frontal cortex can exert cognitive control on these neuromodulatory systems. The cholinergic (ACh) and noradrenergic (NE) systems affect the ability to filter out noise and irrelevant events. We introduce a neural network for action selection that is based on these principles of neuromodulatory systems. The algorithm tested the hypothesis that high levels of serotonin lead to withdrawn behavior by suppressing DA action and that high levels of DA or low levels of 5-HT lead to curious, exploratory behavior. Furthermore, the algorithm tested the idea that top-down signals from the frontal cortex to neuromodulatory areas are critical for an organism to cope with both stressful and novel events. The neural network was implemented on an autonomous robot and tested in an open-field paradigm. The open-field test is often used to test for models anxiety or exploratory behavior in the rodent and allows for qualitative comparisons with the neurorobot's behavior. The present neurorobotic experiments can lead to a better understanding of how neuromodulatory signaling affects the balance between anxious and curious behavior. Therefore, this experimental paradigm may also be informative in exploring a wide range of neurological diseases such as anxiety, autism, attention deficit disorders, and obsessive-compulsive disorders.