Learning From Sparse Demonstrations

doi:10.1109/TRO.2022.3191592

Learning From Sparse Demonstrations

2023-02-01

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DOI：10.1109/TRO.2022.3191592

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Abstract

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In this article, we develop the method of continuous Pontryagin differentiable programming (Continuous PDP), which enables a robot to learn an objective function from a few sparsely demonstrated keyframes. The keyframes, labeled with some time stamps, are the desired task-space outputs, which a robot is expected to follow sequentially. The time stamps of the keyframes can be different from the time of the robot's actual execution. The method jointly finds an objective function and a time-warping function such that the robot's resulting trajectory sequentially follows the keyframes with minimal discrepancy loss. The Continuous PDP minimizes the discrepancy loss using projected gradient descent by efficiently solving the gradient of the robot trajectory with respect to the unknown parameters. The method is first evaluated on a simulated robot arm and then applied to a 6-DoF quadrotor to learn an objective function for motion planning in unmodeled environments. The results show the efficiency of the method, its ability to handle time misalignment between keyframes and robot execution, and the generalization of objective learning into unseen motion conditions.

Keywords:

Robots

Trajectory

Linear programming

Task analysis

Optimal control

Costs

Cost function

Inverse optimal control (IOC)

inverse reinforcement learning (IRL)

learning from demonstrations (LfD)

motion planning

optimal control

Pontryagin differentiable programming (PDP)

Learning From Sparse Demonstrations

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