The Meaning of Action:a review on action recognition and mapping

In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context and outline a possible interpretation of action by taking into account the different aspects of action recognition, action synthesis and task-level planning

Self-supervised online learning of basic object push affordances

Continuous learning of object affordances in a cognitive robot is a challenging problem, the solution to which arguably requires a developmental approach. In this paper, we describe scenarios where robotic systems interact with household objects by pushing them using robot arms while observing the scene with cameras, and which must incrementally learn, without external supervision, both the effect classes that emerge from these interactions as well as a discriminative model for predicting them from object properties. We formalize the scenario as a multi-view learning problem where data co-occur over two separate data views over time, and we present an online learning framework that uses a self-supervised form of learning vector quantization to build the discriminative model. In various experiments, we demonstrate the effectiveness of this approach in comparison with related supervised methods using data from experiments performed using two different robotic platforms

Real-time full body motion imitation on the COMAN humanoid robot

On-line full body imitation with a humanoid robot standing on its own two feet requires simultaneously maintaining the balance and imitating the motion of the demonstrator. In this paper we present a method that allows real-time motion imitation while maintaining stability, based on prioritized task control. We also describe a method of modified prioritized kinematic control that constrains the imitated motion to preserve stability only when the robot would tip over, but does not alter the motions otherwise. To cope with the passive compliance of the robot, we show how to model the estimation of the center of mass of the robot using support vector machines. In the paper we give detailed description of all steps of the algorithm, essentially providing a tutorial on the implementation of kinematic stability control. We present the results on a child-sized humanoid robot called Compliant Humanoid Platform or COMAN. Our implementation shows reactive and stable on-line motion imitation of the humanoid robot

Structural bootstrapping at the sensorimotor level for the fast acquisition of action knowledge for cognitive robots

Autonomous robots are faced with the problem of encoding complex actions (e.g. complete manipulations) in a generic and generalizable way. Recently we had introduced the Semantic Event Chains (SECs) as a new representation which can be directly computed from a stream of 3D images and is based on changes in the relationships between objects involved in a manipulation. Here we show that the SEC framework can be extended (called “extended SEC”) with action-related information and used to achieve and encode two important cognitive properties relevant for advanced autonomous robots: The extended SEC enables us to determine whether an action representation (1) needs to be newly created and stored in its entirety in the robot’s memory or (2) whether one of the already known and memorized action representations just needs to be refined. In human cognition these two processes (1 and 2) are known as accommodation and assimilation. Thus, here we show that the extended SEC representation can be used to realize these processes originally defined by Piaget for the first time in a robotic application. This is of fundamental importance for any cognitive agent as it allows categorizing observed actions in new versus known ones, storing only the relevant aspect

Technical Maturity for Industrial Deployment of Robot Demonstrators

Any technical development done in the context of agile manufacturing has limited benefit if it's not industrially utilized. This requires maturing the developed technologies to a point that they are robust enough to provide a productivity boost, while at the same time adhering to the relevant industrial standards. In this paper we present the various stages in which different robot demonstrators were able to achieve the required technical maturity for industrial deployment. We present the context about the importance of developing technologies that facilitate agile manufacturing followed by the gap between the state of the art and the state of the practice, due to which many promising technologies do not end up being deployed in the industry as they were not subjected to maturity actions required for the transition. We present the journey of four industrial demonstrators that bridged this gap. Furthermore, we provide the assessment methods to ascertain the iterative developmental steps, and present a generic approach to improve the technological readiness.acceptedVersionPeer reviewe