"robot perception" is a perfectly acceptable phrase in written English.
You can use it when referring to the ability of robots to interpret and analyze their environment or the information that they receive. For example, "Robot perception has advanced significantly in recent years, thanks to improvements in artificial intelligence and machine learning."
The papers presented cover a wide range of topics in robotics, spanning mechanisms, kinematics, dynamics and control, human-robot interaction and human-centered systems, distributed systems, mobile systems and mobility, manipulation, field robotics, medical robotics, biological robotics, robot perception, and estimation and learning in robotic systems.
Rudovic, O., Lee, J., Dai, M., Schuller, B., Picard, R. W., " Personalized machine learning for robot perception of affect and engagement in autism therapy," Science Robotics, June 2018.
Papers from a flagship robotics conference that cover topics ranging from kinematics to human-robot interaction and robot perception.
In this talk, I present my work on lightweight and robust robot perception.
As a result, OVROP can provide detailed information on surrounding environment for full-scope and real-time robot perception.
Visual Simultaneous Localization and Mapping (SLAM) based on RGB-D data has developed as a fundamental approach for robot perception over the past decades.
The problem is usually addressed as a supervised classification process, where input data correspond to robot perceptions while classes to semantic categories, like kitchen or corridor.
Theoretical research includes quantification of fundamental capabilities and limitations of feedback systems, development of practical methods and algorithms for decision making under uncertainty, robot sensing and perception, inference and control over networks, as well as architecting and coordinating autonomy-enabled infrastructures for transportation, energy, and beyond.
The start-up behind the robot, Industrial Perception Inc., is the first spinoff of Willow Garage, an ambitious robotics research firm based in Menlo Park, Calif.
In collective robots, the perception capability of a robot node r i is described as a capability vector C i r [17] C_{i}^{r} = diag{ alpha_{i1},aldots{i2},alpha_{impha_{im} } cdot [c_{1},c_{2}, ldots,c_{m} ]^{T} (1 where α ij is the strength of the perception capability c j for robot r i, and [c 1, c 2,…, c m ] T is the perception capability set of the collective robots.
Both applications extend the robot's perception beyond its onboard sensors.
