No abstract

Abstract-

Abstract-This paper proposes an approach to robust state estimation for mobile robots with intermittent dynamics. The approach consists of identifying the robot's mode of operation by classifying the output of onboard sensors into mode-specific contexts. The underlying technique seeks to efficiently use available sensor information to enable accurate, high-bandwidth mode identification. Context classification is combined with multiple-model filtering in order to significantly improve the accuracy of state estimates for hybrid systems. This approach is validated in simulation and shown experimentally to produce accurate estimates on a jogging robot using low-cost sensors.

No abstract

Abstract-In this paper we present our efforts to design a system for feeding back useful haptic information from a highly dynamic running robot to a remote operator using a haptic device. Without adding additional sensors, the legs of the robot are used as feelers to give the operator the capability to both explore and manipulate the robot's environment and to gather meaningful information about properties not captured by visual feedback like weight, movability and structure of an encountered object. We show the capabilities of the system in a user study with both trained and untrained operators.