The agility and adaptability of biological systems are worthwhile goals for next-generation unmanned ground vehicles.
Management of the requisite number of degrees of freedom, however, remains a challenge, as does the ability of an
operator to transfer behavioral intent from human to robot. This paper reviews American Android research funded by
NASA, DARPA, and the U.S. Army that attempts to address these issues. Limb coordination technology, an iterative
form of inverse kinematics, provides a fundamental ability to control balance and posture independently in highly
redundant systems. Goal positions and orientations of distal points of the robot skeleton, such as the hands and feet of a
humanoid robot, become variable constraints, as does center-of-gravity position. Behaviors utilize these goals to
synthesize full-body motion. Biped walking, crawling and grasping are illustrated, and behavior parameterization,
layering and portability are discussed. Robotic skill acquisition enables a show-and-tell approach to behavior
modification. Declarative rules built verbally by an operator in the field define nominal task plans, and neural networks
trained with verbal, manual and visual signals provide additional behavior shaping. Anticipated benefits of the resultant
adaptive collaborative controller for unmanned ground vehicles include increased robot autonomy, reduced operator
workload and reduced operator training and skill requirements.