Abstract The deep supervised and reinforcement learning paradigms (among others) have the potential to endow interactive multimodal social robots with the ability of acquiring skills autonomously. But it is still not very clear yet how they can be best deployed in real world applications. As a step in this direction, we propose a deep learning-based approach for efficiently training a humanoid robot to play multimodal games—and use the game of ‘Noughts and Crosses’ with two variants as a case study. Its minimum requirements for learning to perceive and interact are based on a few hundred example images, a few example multimodal dialogues and physical demonstrations of robot manipulation, and automatic simulations. In addition, we propose novel algorithms for robust visual game tracking and for competitive policy learning with high winning rates, which substantially outperform DQN-based baselines. While an automatic evaluation shows evidence that the proposed approach can be easily extended to new games with competitive robot behaviours, a human evaluation with 130 humans playing with the Pepper robot confirms that highly accurate visual perception is required for successful game play.
There is a similarly limited amount of previous work on humanoid robots playing games against human opponents. Notable exceptions include @cite_18 , where the DB humanoid robot learns to play air hockey using a Nearest Neighbour classifier; @cite_9 , where the Nico humanoid torso robot plays the game of rock-paper-scissors using a Wizzard of Oz' setting; @cite_39 , where the Sky humanoid robot plays catch and juggling using inverse kinematics and induced parameters with least squares linear regression; @cite_28 , where the Nao robot plays a quiz game, an arm imitation game, and a dance game using tabular reinforcement learning; @cite_33 , where the Genie humanoid robot plays the poker game using a Wizard of Oz' setting; and @cite_7 , where the NAO robot plays Checkers using a MinMax search tree. Most of these robots only exhibit non-verbal abilities and are either teleoperated or based on heuristic methods, which suggests that verbal abilities in autonomous trainable robots playing games are underdeveloped. Apart from @cite_4 @cite_24 , we are not aware of any other previous work in humanoid robots playing social games against human opponents and trained with deep learning methods.
@cite_18 @cite_4 @cite_33 @cite_7 @cite_28 @cite_9 @cite_39 @cite_24
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We show that setting a reasonable frame skip can be critical to the performance of agents learning to play Atari 2600 games. In all of the six games in our experiments, frame skip is a strong determinant of success. For two of these games, setting a large frame skip leads to state-of-the-art performance. The rate at which an agent interacts with its environment may be critical to its success. In the Arcade Learning Environment (ALE) ( 2013) games run at sixty frames per second, and agents can submit an action at every frame. Frame skip is the number of frames an action is repeated before a new action is selected. Existing reinforcement learning (RL) approaches use static frame skip: HNEAT ( 2013) uses a frame skip of 0; DQN ( 2013) uses a frame skip of 2-3; SARSA and planning approaches ( 2013) use a frame skip of 4. When action selection is computationally intensive, setting a higher frame skip can significantly decrease the time it takes to simulate an episode, at the cost of missing opportunities that only exist at a finer resolution. A large frame skip can also prevent degenerate super-human-reflex strategies, such as those described by for Bowling, Kung Fu Master, Video Pinball and Beam Rider. We show that in addition to these advantages agents that act with high frame skip can actually learn faster with respect to the number of training episodes than those that skip no frames. We present results for six of the seven games covered by : three (Beam Rider, Breakout and Pong) for which DQN was able to achieve near- or superhuman performance, and three (Q*Bert, Space Invaders and Seaquest) for which all RL approaches are far from human performance. These latter games were understood to be difficult because they require ‘strategy that extends over long time scales.’ In our experiments, setting a large frame skip was critical to achieving state-of-the-art performance in two of these games: Space Invaders and Q*Bert. More generally, the frame skip parameter was a strong determinant of performance in all six games. Our learning framework is a variant of Enforced Subpopulations (ESP) (Gomez and Miikkulainen 1997), a neuroevolution approach that has been successfully imple Recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. This makes it increasingly difficult and inconvenient to program these tasks manually. Furthermore, humanoid robots, in contrast to industrial robots, should in the distant future behave within a social environment. Therefore, it must be possible to extend the robot's abilities in an easy and natural way. To address these requirements, this work investigates the topic of imitation learning of motor skills. The focus lies on providing a humanoid robot with the ability to learn new bi-manual tasks through the observation of object trajectories. For this, an imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models. The learned information is used to initialize an attractor-based movement generation algorithm that optimizes the reproduced movement towards the fulfillment of additional criteria, such as collision avoidance. Experiments performed with the humanoid robot ASIMO show that the proposed system is suitable for transferring information from a human demonstrator to the robot. These results provide a good starting point for more complex and interactive learning tasks. We describe a learning strategy that allows a humanoid robot to autonomously build a representation of its workspace: we call this representation Reachable Space Map. Interestingly, the robot can use this map to: (i) estimate the Reachability of a visually detected object (i.e. judge whether the object can be reached for, and how well, according to some performance metric) and (ii) modify its body posture or its position with respect to the object to achieve better reaching. The robot learns this map incrementally during the execution of goal-directed reaching movements; reaching control employs kinematic models that are updated online as well. Our solution is innovative with respect to previous works in three aspects: the robot workspace is described using a gaze-centered motor representation, the map is built incrementally during the execution of goal-directed actions, learning is autonomous and online. We implement our strategy on the 48-DOFs humanoid robot Kobian and we show how the Reachable Space Map can support intelligent reaching behavior with the whole-body (i.e. head, eyes, arm, waist, legs). Learning to perform household tasks is a key step towards developing cognitive service robots. This requires that robots are capable of discovering how to use human-designed products. In this paper, we propose an active learning approach for acquiring object affordances and manipulation skills in a bottom-up manner. We address affordance learning in continuous state and action spaces without manual discretization of states or exploratory motor primitives. During exploration in the action space, the robot learns a forward model to predict action effects. It simultaneously updates the active exploration policy through reinforcement learning, whereby the prediction error serves as the intrinsic reward. By using the learned forward model, motor skills are obtained to achieve goal states of an object. We demonstrate through real-world experiments that a humanoid robot NAO is able to autonomously learn how to manipulate two types of garbage cans with lids that need to be opened and closed by different motor skills.