Parametric Hidden Markov Models for Recognition and Synthesis of Movements

In humanoid robotics, the recognition and synthesis of parametric movements plays an extraordinary role for robot human interaction. Such a parametric movement is a movement of a particular type (semantic), for example, similar pointing movements performed at different table-top positions. For understanding the whole meaning of a movement of a human, the recognition of its type, likewise its parameterization are important. Only both together convey the whole meaning. Vice versa, for mimicry, the synthesis of movements for the motor control of a robot needs to be parameterized, e.g., by the relative position a grasping action is performed at. For both cases, synthesis and recognition, only parametric approaches are meaningful as it is not feasible to store, or acquire all possible trajectories. In this paper, we use hidden Markov models (HMMs) extended in an exemplar-based parametric way (PHMM) to represent parametric movements. As HMMs are generative, they are well suited for synthesis as well as for recognition. Synthesis and recognition are carried out through interpolation of exemplar movements to generalize over the parameterization of a movement class. In the evaluation of the approach we concentrate on a systematical validation for two parametric movements, grasping and pointing. Even though the movements are very similar in appearance our approach is able to distinguish the two movement types reasonable well. In further experiments, we show the applicability for online recognition based on very noisy 3D tracking data. The use of a parametric representation of movements is shown in a robot demo, where a robot removes objects from a table as demonstrated by an advisor. The synthesis for motor control is performed for arbitrary table-top positions

Advantages and limitations of reservoir computing on model learning for robot control

In certain cases analytical derivation of physicsbased models of robots is difficult or even impossible. A potential workaround is the approximation of robot models fromsensor data-streams employing machine learning approaches.In this paper, the inverse dynamics models are learned byemploying a learning algorithm, introduced in [1], which isbased on reservoir computing in conjunction with self-organizedlearning and Bayesian inference. The algorithm is evaluatedand compared to other state of the art algorithms in termsof generalization ability, convergence and adaptability usingfive datasets gathered from four robots in order to investigateits pros and cons. Results show that the proposed algorithmcan adapt in real-time changes of the inverse dynamics modelsignificantly better than the other state of the art algorithms

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

Integrating Mission and Task Planning in an Industrial Robotics Framework

This paper presents a framework developed for an industrial robotics system that utilises two different planning components. At a high level, a multi-robot mission planner interfaces with a fleet and environment manager and uses multiagent planning techniques to build mission assignments to be distributed to a robot fleet. On each robot, a task planner automatically converts the robot's world model and skill definitions into a planning problem which is then solved to find a sequence of actions that the robot should perform to complete its mission. This framework is demonstrated on an industrial kitting task in a real-world factory environment

Structural investigations on bredigite from the Hatrurim Complex

Bredigite, Сa7Mg(SiO4)4, is an indicator mineral of metasomatic rocks of the sanidinite facies formed at high temperatures (>800 °C) and low pressures (<1–2 kbar). Bredigite samples from ternesite-gazeevite-larnite pyrometamorphic rocks of the Hatrurim Complex (Negev Desert, Israel) have been studied by electron probe micro analysis and single-crystal diffraction using synchrotron radiation. They are characterized by a relatively uniform composition. The empirical formula calculated on the basis of 16 O atoms per formula unit is: (Ca7.006Na0.015Ba0.014)Σ7.035Mg0.938(Si4.000P0.014)Σ4.014O16. Basic crystallographic data of a sample studied by X-ray diffraction are as follows: orthorhombic symmetry, space group Pnnm, a = 18.38102(17) Å, b = 6.74936(7) Å, c = 10.90328(11) Å, V = 1352.66(2) Å3, Z = 4. Structure solution and subsequent least-squares refinements resulted in a residual of R(|F|) = 0.023 for 2584 independent observed reflections with I > 2σ(I) and 149 parameters. To the best of our knowledge this is the first detailed structural investigation on natural bredigite. In contrast to previous studies on samples retrieved from metallurgical slags there was no need to describe the structure in the acentric space group Pnn2. Furthermore, the problem of Ba incorporation into the bredigite structure is discussed. Data on the composition of Ba-bearing bredigites from pyrometamorphic rocks of the Hatrurim Complex from Jordan with simplified formula Ba0.7Ca13.3Mg2(SiO4)8 (based on 32 oxygen atoms) are provided for the first time, pointing out perspectives of finding new Ba-bearing minerals isostructural with bredigite in nature