Active Vision for Scene Understanding

Visual perception is one of the most important sources of information for both humans and robots. A particular challenge is the acquisition and interpretation of complex unstructured scenes. This work contributes to active vision for humanoid robots. A semantic model of the scene is created, which is extended by successively changing the robot\u27s view in order to explore interaction possibilities of the scene

Active Vision for Scene Understanding

Visual perception is one of the most important sources of information for both humans and robots. A particular challenge is the acquisition and interpretation of complex unstructured scenes. This work contributes to active vision for humanoid robots. A semantic model of the scene is created, which is extended by successively changing the robot's view in order to explore interaction possibilities of the scene

BlueSky: Combining Task Planning and Activity-Centric Access Control for Assistive Humanoid Robots

In the not too distant future, assistive humanoid robots will provide versatile assistance for coping with everyday life. In their interactions with humans, not only safety, but also security and privacy issues need to be considered. In this Blue Sky paper, we therefore argue that it is time to bring task planning and execution as a well-established field of robotics with access and usage control in the field of security and privacy closer together. In particular, the recently proposed activity-based view on access and usage control provides a promising approach to bridge the gap between these two perspectives. We argue that humanoid robots provide for specific challenges due to their task-universality and their use in both, private and public spaces. Furthermore, they are socially connected to various parties and require policy creation at runtime due to learning. We contribute first attempts on the architecture and enforcement layer as well as on joint modeling, and discuss challenges and a research roadmap also for the policy and objectives layer. We conclude that the underlying combination of decentralized systems\u27 and smart environments\u27 research aspects provides for a rich source of challenges that need to be addressed on the road to deployment

Revenue Management for Communication Satellite Operators - Opportunities and Challenges

© 2020 IEEE. In this paper we propose a Revenue Management framework for satcom operators and show with a proof-of-concept simulation that predicts a significant gain in revenues. New satellite operators, highly variable demand for data, digital payloads, and new phased array technologies are likely to remake the current satcom landscape. One of the challenges operators old and new will face is how to manage demand and capacity. Airlines faced a similar situation with deregulation in the 1970s - their response with tiered pricing and seat inventory control to allocate capacity (known as Revenue Management), which may offer lessons for the satcom market. The satcom industry shares many characteristics with the airline industry, such as inflexible capacity, low marginal sales cost, perishable inventory, heterogenous customers, and variable and uncertain demand. Generally, those characteristics favor the implementation of a Revenue Management system. However, the details of how Revenue Management can be used by satcom operators still need to be explored, which is the focus of this paper

Autonomous view selection and gaze stabilization for humanoid robots

To increase the autonomy of humanoid robots, the visual perception must support the efficient collection and interpretation of visual scene cues by providing task-dependent information. Active vision systems allow to extend the observable workspace by employing active gaze control, i.e. by shifting the gaze to relevant areas in the scene. When moving the eyes, stabilization of the camera images is crucial for successful task execution. In this paper, we present an active vision system for task-oriented selection of view directions and gaze stabilization to enable a humanoid robot to robustly perform vision-based tasks. We investigate the interaction between a gaze stabilization controller and view planning to select the next best view direction based on saliency maps which encode task-relevant information. We demonstrate the performance of the systems in a real world scenario, in which a humanoid robot is performing vision-based grasping while moving, a task that would not be possible without the combination of view selection and gaze stabilization

Multimodal gaze stabilization of a humanoid robot based on reafferences

Gaze stabilization is fundamental for humanoid robots. By stabilizing vision, it enhances perception of the environment and keeps regions of interest inside the field of view. In this contribution, a multimodal gaze stabilization combining proprioceptive, inertial and visual cues is introduced. It integrates a classical inverse kinematic control with vestibulo-ocular and optokinetic reflexes. Inspired by neuroscience, our contribution implements a forward internal model that modulates the reflexes based on the reafference principle. This principle filters self-generated movements out of the reflexive feedback loop. The versatility and effectiveness of this method are experimentally validated on the ARMAR-III humanoid robot. We first demonstrate that all the stabilization mechanisms (inverse kinematics and reflexes) are complementary. Then, we show that our multimodal method, combining these three modalities with the reafference principle, provides a versatile gaze stabilizer able to handle a large panel of perturbations