A novel method of tactile communication among human-robot and robot-robot collaborative teams is developed for the purpose of adaptive grasp control of dexterous robotic hands. Neural networks are applied to the problem of classifying the direction objects slide against different tactile fingertip sensors in real-time. This ability to classify the direction that an object slides in a dexterous robotic hand was used for adaptive grasp synergy control to afford context dependent robotic reflexes in response to the direction of grasped object slip. Case studies with robot-robot and human-robot collaborative teams successfully demonstrated the feasibility; when object slip in the direction of gravity (towards the ground) was detected, the dexterous hand increased the grasp force to prevent dropping the object. When a human or robot applied an upward force to cause the grasped object to slip upward, the dexterous hand was programmed to release the object into the hand of the other team member. This method of adaptive grasp control using direction of slip detection can improve the efficiency of human-robot and robot-robot teams. I. INTRODUCTIONShortly after the advent of the artificial robotic hand, preventing grasped objects from being inadvertently dropped has been a priority [1]. This is a very important problem in the area of prosthetic hands, as limb-absent people do not have a direct sense of the grasp force applied