CMPack'04: Robust Modeling through Vision and Learning

The CMPACK’04 team follows our past teams CMPack’03, CMPack’02, CMPack’01, CMPack’00, CMTrio’99, and CMTrio’98 [7, 4, 11, 10]. In our research this year, we explored the capabilities of the new ERS-7 robotic platform; developed new ways of modeling the world, such as using accelerometer data to detect when the robot is entangled; and maintained our focus on robust behaviors and cooperation. In this paper, we will focus in detail on several recent components of the team. First, we will describe our local model, which traces radial lines across vision frames to efficiently construct a model of the environment [5]. Next, we will describe how accelerometer data can provide a rich source of information about the world; specifically, we will show how to use accelerometer data to detect when the robot is entangled, and, as an aside, how this same algorithm can be used to identify the surface under the robot [9]. Finally, we will describe how the robot learns the effects of its different kicks by experimentation and how it then applies this knowledge to select the appropriate kick from our behaviors [3].