THESIS
2023
1 online resource (xii, 52 pages) : color illustrations
Abstract
The control problem with the humanoid model has been a challenging question. To develop a suitable control system for the humanoid model to complete human tasks, a
recent approach is imitation learning. Generative Adversarial Imitation Learning (GAIL)
is the state-of-the-art imitation learning framework available. With the Imitation from
Observation (IfO) variant, the GAIL framework can learn to imitate motion from the
mocap data for the humanoid model. Since the GAIL framework requires the target
motion to be recorded with the expensive mocap system, it is hard for GAIL to control
the humanoid model to imitate new motion that is not recorded with the mocap system.
Most of the time the GAIL framework can only imitate one single target motion, which
makes it impossible to develop a cont...[
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The control problem with the humanoid model has been a challenging question. To develop a suitable control system for the humanoid model to complete human tasks, a
recent approach is imitation learning. Generative Adversarial Imitation Learning (GAIL)
is the state-of-the-art imitation learning framework available. With the Imitation from
Observation (IfO) variant, the GAIL framework can learn to imitate motion from the
mocap data for the humanoid model. Since the GAIL framework requires the target
motion to be recorded with the expensive mocap system, it is hard for GAIL to control
the humanoid model to imitate new motion that is not recorded with the mocap system.
Most of the time the GAIL framework can only imitate one single target motion, which
makes it impossible to develop a control system that can complete human tasks, since
those tasks may require the skill of multiple target motions. Inspired by how a human
learns a new motion, the framework proposed in this thesis uses the primary motion which
is recorded in mocap data and learns to imitate the unstable target motion taped in video
clips with the assistance of the learned skill of the primary motion. This framework also
introduces the idea of the external control signal such that the framework can change to
imitate different target motions in real-time. With the aid of the primary motion, the
proposed framework can deliver a stable and human-like motion that imitates multiple
target motions taped in inaccurate video clips. With this framework, the humanoid model
can learn to imitate different target motions from low-cost sources and can interchange
the imitating target motion in real-time to complete the human task.
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