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Since humanoid robots have similar body structures to humans, a humanoid robot is expected to perform various dynamic tasks including object manipulation. This research focuses on issues related to learning and performing object manipulation. Basic motion primitives for tasks are learned from observation of human's behaviors. An object manipulation task is divided into two types of motion primitives, which are represented as hidden Markov models (HMMs): one for a body motion primitive and the other for the relation between the object and body parts, which manipulate the object. When performing a task, a natural whole body motion is associated from an object motion by using learned motion primitives. Furthermore, the associated body motion is reshaped in both spatial and temporal space, in a more precise way. The reshaping in spatial space is realized in two stages by a feedback control policy learned with reinforcement learning and by constrained inverse kinematics. Key features like end-effectors for manipulation and timing for a task are extracted and used for the feedback control policy learning. The reshaping in temporal space is realized by comparing a predicted and observed object motion speed.