Jonathan Juett and Benjamin Kuipers. 2018.
Learning to Grasp by Extending the Peri-Personal Space Graph.
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2018.
We present a robot model of early reach and grasp learning, inspired by infant learning without prior knowledge of the geometry, kinematics, or dynamics of the arm.
Human infants at reach onset are capable of using a sequence of jerky submotions to bring the hand to the position of a nearby object. A robotic learning agent can produce qualitatively similar behavior by using a graph representation to encode a set of safe, potentially useful arm states and feasible moves between them. These observations show that the Peri-Personal Space (PPS) Graph model is sufficient for early reaching and suggest that infants may use analogous models during this phase.
In this paper, we show that the PPS Graph, with a simulated Palmar reflex (a reflex in infants that closes the fingers when the palm is touched), allows accidental grasps to occur during continued reaching practice. Given these occasional events, the agent can bootstrap to a simple deliberate grasp action. In particular, the agent must learn three new necessary conditions for a grasp: the hand should be open as the grasp begins, the final motion of the hand should be led by the gripper opening so that it reaches the target first, and the wrist must be oriented such that the gripper fingers may close around the target object, often requiring the opening to be perpendicular to the object's major axis.
Combined with the existing capability to reach and interact with target objects, knowledge of these conditions allows the agent to learn increasingly reliable purposeful grasps. The first two conditions are addressed in this paper, and allow 45% of grasps to succeed.
This work contributes toward the larger goal of foundational robot learning after the model of infant learning, with minimal prior knowledge of its own anatomy or its environment. The ability to grasp will allow the agent to control the motion and position of objects, providing a richer representation for its environment and new experiences to learn from.