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Robotic systems are ever more capable of automation and fulfilment of complex tasks, particularly with reliance on recent advances in intelligent systems, deep learning and artificial intelligence. However, as robots and humans come closer in their interactions, the matter of interpretability, or explainability of robot decision-making processes for the human grows in importance. A successful interaction and collaboration will only take place through mutual understanding of underlying representations of the environment and the task at hand. This is currently a challenge in deep learning systems. We present a hierarchical deep reinforcement learning system, consisting of a low-level agent handling the large actions/states space of a robotic system efficiently, by following the directives of a high-level agent which is learning the highlevel dynamics of the environment and task. This high-level agent forms a representation of the world and task at hand that is interpretable for a human operator. The method, which we call Dot-to-Dot, is tested on a MuJoCo-based model of the Fetch Robotics Manipulator, as well as a Shadow Hand, to test its performance. Results show efficient learning of complex actions/states spaces by the low-level agent, and an interpretable representation of the task and decision-making process learned by the high-level agent.

The problem of common sense remains a major obstacle to progress in artificial intelligence. Here, we argue that common sense in humans is founded on a set of basic capacities that are possessed by many other animals, capacities pertaining to the understanding of objects, space, and causality. The field of animal cognition has developed numerous experimental protocols for studying these capacities and, thanks to progress in deep reinforcement learning (RL), it is now possible to apply these methods directly to evaluate RL agents in 3D environments. Besides evaluation, the animal cognition literature offers a rich source of behavioural data, which can serve as inspiration for RL tasks and curricula. Common Sense before LanguageThe challenge of endowing computers with common sense has been seen as a major obstacle to achieving the boldest aims of artificial intelligence (AI) since the field's earliest days [1] and it remains a significant problem today [2-6]. There is no universally accepted definition of common sense. However, most authors use language as a touchstone, following the example of [1], who stated that '[a] program has common sense if it automatically deduces for itself a sufficiently wide class of immediate consequences of anything it is told and what it already knows'. Consequently, tests for common sense are typically language based. For example, one such test uses so-called 'Winograd schemas' [7][8][9]. These are pairs of sentences that differ by a single word and contain an ambiguous pronoun whose resolution depends on understanding some aspect of common sense. Consider the sentences 'The falling rock smashed the bottle because it was heavy' and 'The falling rock smashed the bottle because it was fragile'. The pronoun 'it' refers to the rock in the first sentence, but to the bottle in the second. We are able to resolve the pronoun correctly in each case because of our common sense understanding of falling and fragility. In this paper, by contrast, we will set language temporarily to one side and focus on common sense capacities that are also found in non-human animals. Our rationale is that these capacities are also the foundation for human common sense. They are, so to speak, conceptually prior to language and human language rests on the foundation they provide [10]. HighlightsEndowing computers with common sense remains one of the biggest challenges in the field of artificial intelligence (AI).

Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing (such as Chess and Go) towards ability-oriented testing, in which AI systems are tested on their capacity to solve certain kinds of novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6–10 (n=52). We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, ‘ironbar’ and ‘Trrrrr’, from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6–10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines.