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This paper develops an estimation framework for sensor-guided dual-arm manipulation of a rigid object. Using an unscented Kalman Filter (UKF), the approach combines both visual and kinesthetic information to track both the manipulators and object. From visual updates of the object and manipulators, and tactile updates, the method estimates both the robot's internal state and the object's pose. Nonlinear...
This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge...
This paper develops a method to fuse stereo vision, force-torque sensor, and joint angle encoder measurements to estimate and track the location of a grasped object within the hand. We pose the problem as a hybrid systems estimation problem, where the continuous states are the object 6D pose, finger contact location, wrist-to-camera transform and the discrete states are the finger contact modes with...
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