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Social affordances, although key in human-robot interaction processes, have received little attention in robotics. Hence, it remains unclear whether the prevailing mechanisms to exploit and learn affordances in the absence of human interaction can be extended to affordances in social contexts. This study provides a review of the concept of affordance in psychology and robotics and proposes a new view on social affordances in robotics and their differences from physical affordances. We moreover show how the model-based reinforcement learning theory provides a useful framework to study and compare social and physical affordances. To further study their differences, we present a new benchmark task mixing navigation and social interaction, in which a robot has to make a human follow and reach different goal positions in a row. This new task is solved in simulation using a modular architecture and reinforcement learning.

Experience replay is widely used in AI to bootstrap reinforcement learning (RL) by enabling an agent to remember and reuse past experiences. Classical techniques include shuffled-, reversed-ordered- and prioritized-memory buffers, which have different properties and advantages depending on the nature of the data and problem. Interestingly, recent computational neuroscience work has shown that these techniques are relevant to model hippocampal reactivations recorded during rodent navigation. Nevertheless, the brain mechanisms for orchestrating hippocampal replay are still unclear. In this paper, we present recent neurorobotics research aiming to endow a navigating robot with a neuro-inspired RL architecture (including different learning strategies, such as model-based (MB) and model-free (MF), and different replay techniques). We illustrate through a series of numerical simulations how the specificities of robotic experimentation (e.g., autonomous state decomposition by the robot, noisy perception, state transition uncertainty, non-stationarity) can shed new lights on which replay techniques turn out to be more efficient in different situations. Finally, we close the loop by raising new hypotheses for neuroscience from such robotic models of hippocampal replay.

Abstract In a volatile environment where rewards are uncertain, successful performance requires a delicate balance between exploitation of the best option and exploration of alternative choices. It has theoretically been proposed that dopamine contributes to the control of this exploration-exploitation trade-off, specifically that the higher the level of tonic dopamine, the more exploitation is favored. We demonstrate here that there is a formal relationship between the rescaling of dopamine positive reward prediction errors and the exploration-exploitation trade-off in simple non-stationary multi-armed bandit tasks. We further show in rats performing such a task that systemically antagonizing dopamine receptors greatly increases the number of random choices without affecting learning capacities. Simulations and comparison of a set of different computational models (an extended Q-learning model, a directed exploration model, and a meta-learning model) fitted on each individual confirm that, independently of the model, decreasing dopaminergic activity does not affect learning rate but is equivalent to an increase in random exploration rate. This study shows that dopamine could adapt the exploration-exploitation trade-off in decision-making when facing changing environmental contingencies.

Virtual reality (VR) technologies allow for the creation of 3D environments that can be exploited at the human level, maximizing humans’ use of perceptual skills through their sensory channels, and enabling them to actively influence the course of events that take place in the virtual environment (VE). As such, they constitute a significant asset in the treatment of post-traumatic stress disorder (PTSD) via exposure therapy. In this article, we review the VR tools that have been developed to date for the treatment of PTSD. The article aims to analyze how VR technologies can be exploited from a sensorimotor and interactive perspective. The findings from this analysis suggest a significant emphasis on sensory stimulation to the detriment of interaction. Finally, we propose new ideas regarding the more successful integration of sensorimotor activities and interaction into VR exposure therapy for PTSD.

Reinforcement learning (RL) aims at building a policy that maximizes a task-related reward within a given domain. When the domain is known, i.e., when its states, actions and reward are defined, Markov Decision Processes (MDPs) provide a convenient theoretical framework to formalize RL. But in an open-ended learning process, an agent or robot must solve an unbounded sequence of tasks that are not known in advance and the corresponding MDPs cannot be built at design time. This defines the main challenges of open-ended learning: how can the agent learn how to behave appropriately when the adequate states, actions and rewards representations are not given? In this paper, we propose a conceptual framework to address this question. We assume an agent endowed with low-level perception and action capabilities. This agent receives an external reward when it faces a task. It must discover the state and action representations that will let it cast the tasks as MDPs in order to solve them by RL. The relevance of the action or state representation is critical for the agent to learn efficiently. Considering that the agent starts with a low level, task-agnostic state and action spaces based on its low-level perception and action capabilities, we describe open-ended learning as the challenge of building the adequate representation of states and actions, i.e., of redescribing available representations. We suggest an iterative approach to this problem based on several successive Representational Redescription processes, and highlight the corresponding challenges in which intrinsic motivations play a key role.

Despite major progress in Robotics and AI, robots are still basically “zombies” repeatedly achieving actions and tasks without understanding what they are doing. Deep-Learning AI programs classify tremendous amounts of data without grasping the meaning of their inputs or outputs. We still lack a genuine theory of the underlying principles and methods that would enable robots to understand their environment, to be cognizant of what they do, to take appropriate and timely initiatives, to learn from their own experience and to show that they know that they have learned and how. The rationale of this paper is that the understanding of its environment by an agent (the agent itself and its effects on the environment included) requires its self-awareness, which actually is itself emerging as a result of this understanding and the distinction that the agent is capable to make between its own mind-body and its environment. The paper develops along five issues: agent perception and interaction with the environment; learning actions; agent interaction with other agents–specifically humans; decision-making; and the cognitive architecture integrating these capacities.

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results; however, computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested and are hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.

Post-Traumatic Stress Disorder (PTSD) can be seen as the result of dysfunctional beliefs that associate stimuli with a danger or a threat leading to anxious reactions. Exposure therapy is so far considered to be the most effective treatment, and research suggests that it is mainly based on a habituation process. Based on learning theories, it appears that a passive systemic exposure to traumatic stimuli should not be the best option for the treatment of PTSD. We hypothesis that an active learning of safer and healthier coping strategies combined with systematic exposure should be more effective in reducing the psychological distress associated with PTSD. In this paper, we describe the theoretical foundations of this approach that focuses on the action and activity of the patient in his or her exposure environment. In this approach, we take advantage of Virtual Reality technologies and learning mechanics of serious games to allow the patient to learn new safe associations while promoting the empowerment. We named this action-centered exposure therapy (ACET). This approach exploits behaviorism, cognitivism, and constructivism learning theories. With the different benefits of virtual reality technologies, this approach would easily integrate with in-virtuo exposure therapy and would allow us to exploit as much as possible the enormous potential of these technologies. As a first step toward validation, we present a case study that supports the ACET approach.

Perceptual decision making has been widely studied using tasks in which subjects are asked to discriminate a visual stimulus and instructed to report their decision with a movement. In these studies, performance is measured by assessing the accuracy of the participants' choices as a function of the ambiguity of the visual stimulus. Typically, the reporting movement is considered as a mere means of reporting the decision with no influence on the decision-making process. However, recent studies have shown that even subtle differences of biomechanical costs between movements may influence how we select between them. Here we investigated whether this purely motor cost could also influence decisions in a perceptual discrimination task in detriment of accuracy. In other words, are perceptual decisions only dependent on the visual stimulus and entirely orthogonal to motor costs? Here we show the results of a psychophysical experiment in which human subjects were presented with a random dot motion discrimination task and asked to report the perceived motion direction using movements of different biomechanical cost. We found that the pattern of decisions exhibited a significant bias towards the movement of lower cost, even when this bias reduced performance accuracy. This strongly suggests that motor costs influence decision making in visual discrimination tasks for which its contribution is neither instructed nor beneficial.