Learning to recognize parallel combinations of human motion primitives with linguistic descriptions using non-negative matrix factorization

International audienceWe present an approach, based on non-negative matrix factorization, for learning to recognize parallel combinations of initially unknown human motion primitives, associated with ambiguous sets of linguistic labels during training. In the training phase, the learner observes a human producing complex motions which are parallel combinations of initially unknown motion primitives. Each time the human shows a complex motion, he also provides high-level linguistic descriptions, consisting of a set of labels giving the name of the primitives inside the complex motion. From the observation of multi-modal combinations of high-level labels with high-dimensional continuous unsegmented values representing complex motions, the learner must later on be able to recognize, through the production of the adequate set of labels, which are the motion primitives in a novel complex motion produced by a human, even if those combinations were never observed during training. We explain how this problem, as well as natural extensions, can be addressed using non-negative matrix factorization. Then, we show in an experiment in which a learner has to recognize the primitive motions of complex human dance choreographies, that this technique allows the system to infer with good performance the combinatorial structure of parallel combinations of unknown primitives

Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping

This paper presents a novel real-time method for tracking salient closed boundaries from video image sequences. This method operates on a set of straight line segments that are produced by line detection. The tracking scheme is coherently integrated into a perceptual grouping framework in which the visual tracking problem is tackled by identifying a subset of these line segments and connecting them sequentially to form a closed boundary with the largest saliency and a certain similarity to the previous one. Specifically, we define a new tracking criterion which combines a grouping cost and an area similarity constraint. The proposed criterion makes the resulting boundary tracking more robust to local minima. To achieve real-time tracking performance, we use Delaunay Triangulation to build a graph model with the detected line segments and then reduce the tracking problem to finding the optimal cycle in this graph. This is solved by our newly proposed closed boundary candidates searching algorithm called "Bidirectional Shortest Path (BDSP)". The efficiency and robustness of the proposed method are tested on real video sequences as well as during a robot arm pouring experiment.Comment: 7 pages, 8 figures, The 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) submission ID 103

Fostering Progress in Performance Evaluation and Benchmarking of Robotic and Automation Systems

We have shared benchmarks for many engineering systems and products in the market that can be used to compare solutions and systems. We can compare cars in terms of maximum speed, acceleration, and maximum torque; computers in terms of flops, random access memory, and hard disk capacity; and smartphones in terms of battery life and screen dimensions. We also have shared usability metrics based on human factors, which are used to compare the ease of use of different software interfaces. When we come to the evaluation and the comparison of how intelligent, robust, adaptive, and antifragile the behaviors of robots are in performing a given set of tasks, such as daily life activities with daily life objects such as in a kitchen or a hospital room, we are in trouble

Cooperative Relative Positioning of Mobile Users by Fusing IMU Inertial and UWB Ranging Information

Relative positioning between multiple mobile users is essential for many applications, such as search and rescue in disaster areas or human social interaction. Inertial-measurement unit (IMU) is promising to determine the change of position over short periods of time, but it is very sensitive to error accumulation over long term run. By equipping the mobile users with ranging unit, e.g. ultra-wideband (UWB), it is possible to achieve accurate relative positioning by trilateration-based approaches. As compared to vision or laser-based sensors, the UWB does not need to be with in line-of-sight and provides accurate distance estimation. However, UWB does not provide any bearing information and the communication range is limited, thus UWB alone cannot determine the user location without any ambiguity. In this paper, we propose an approach to combine IMU inertial and UWB ranging measurement for relative positioning between multiple mobile users without the knowledge of the infrastructure. We incorporate the UWB and the IMU measurement into a probabilistic-based framework, which allows to cooperatively position a group of mobile users and recover from positioning failures. We have conducted extensive experiments to demonstrate the benefits of incorporating IMU inertial and UWB ranging measurements.Comment: accepted by ICRA 201