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

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