In principle, reinforcement learning and policy search methods can enable robots to learn highly complex and general skills that may allow them to function amid the complexity and diversity of the real world. However, training a policy that generalizes well across a wide range of real-world conditions requires far greater quantity and diversity of experience than is practical to collect with a single robot. Fortunately, it is possible for multiple robots to share their experience with one another, and thereby, learn a policy collectively. In this work, we explore distributed and asynchronous policy learning as a means to achieve generalization and improved training times on challenging, real-world manipulation tasks. We propose a distributed and asynchronous version of Guided Policy Search and use it to demonstrate collective policy learning on a vision-based door opening task using four robots. We show that it achieves better generalization, utilization, and training times than the single robot alternative.
Robotic motor skill learning has shown considerable promise for enabling robots to autonomously learn complex motion skills @cite_25 @cite_15 @cite_10 @cite_11 . However, most successes in robotic motor skill learning have involved significant manual design of representations in order to enable policies to generalize effectively. For example, the well-known dynamic movement primitive representation @cite_23 has been widely used to generalize learned skills by adapting the goal state, but it inherently restricts the learning process to trajectory-centric behaviors.
@cite_23 @cite_15 @cite_10 @cite_25 @cite_11
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Learning new motor tasks from physical interactions is an important goal for both robotics and machine learning. However, when moving beyond basic skills, most monolithic machine learning approaches fail to scale. For more complex skills, methods that are tailored for the domain of skill learning are needed. In this paper, we take the task of learning table tennis as an example and present a new framework that allows a robot to learn cooperative table tennis from physical interaction with a human. The robot first learns a set of elementary table tennis hitting movements from a human table tennis teacher by kinesthetic teach-in, which is compiled into a set of motor primitives represented by dynamical systems. The robot subsequently generalizes these movements to a wider range of situations using our mixture of motor primitives approach. The resulting policy enables the robot to select appropriate motor primitives as well as to generalize between them. Finally, the robot plays with a human table tennis partner and learns online to improve its behavior. We show that the resulting setup is capable of playing table tennis using an anthropomorphic robot arm. A critical capability required for generally intelligent robot behavior is the ability to sequence motor skills to reach a goal. This requires a (typically abstract) representation that supports goal-directed planning, which raises the question of how to construct such a representation. Previous work has addressed this question in the context of simple black-box motor skills, which are insufficiently flexible to support the wide range of behavior required of intelligent robots. We now extend that work to include parametrized motor skills, where a robot must both select an action to execute and also decide how to parametrize it. We show how to construct a representation suitable for planning with parametrized motor skills, and specify conditions which are sufficient to separate the selection of motor skills from the parametrization of those skills. Our method results in a simple discrete abstract representation for planning followed by a parameter selection process that operates on a fixed plan. We first demonstrate learning this representation in a virtual domain based on Angry Birds and then learn an abstract symbolic representation for a robot manipulation task. 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. To engage in cooperative activities with human partners, robots have to possess basic interactive abilities and skills. However, programming such interactive skills is a challenging task, as each interaction partner can have different timing or an alternative way of executing movements. In this paper, we propose to learn interaction skills by observing how two humans engage in a similar task. To this end, we introduce a new representation called Interaction Primitives. Interaction primitives build on the framework of dynamic motor primitives (DMPs) by maintaining a distribution over the parameters of the DMP. With this distribution, we can learn the inherent correlations of cooperative activities which allow us to infer the behavior of the partner and to participate in the cooperation. We will provide algorithms for synchronizing and adapting the behavior of humans and robots during joint physical activities.