Inverse Optimal Control with Incomplete Observations

by   Wanxin Jin, et al.

In this article, we consider the inverse optimal control problem given incomplete observations of an optimal trajectory. We hypothesize that the cost function is constructed as a weighted sum of relevant features (or basis functions). We handle the problem by proposing the recovery matrix, which establishes a relationship between available observations of the trajectory and weights of given candidate features. The rank of the recovery matrix indicates whether a subset of relevant features can be found among the candidate features and the corresponding weights can be recovered. Additional observations tend to increase the rank of the recovery matrix, thus enabling cost function recovery. We also show that the recovery matrix can be computed iteratively. Based on the recovery matrix, a methodology for using incomplete observations of the trajectory to recover the weights of specified features is established, and an efficient algorithm for recovering the feature weights by finding the minimal required observations is developed. We apply the proposed algorithm to learning the cost function of a simulated robot manipulator conducting free-space motions. The results demonstrate the stable, accurate and robust performance of the proposed approach compared to state of the art techniques.


page 1

page 2

page 3

page 4


Inverse Optimal Control from Demonstration Segments

This paper develops an inverse optimal control method to learn an object...

A Robustness Analysis of Inverse Optimal Control of Bipedal Walking

Cost functions have the potential to provide compact and understandable ...

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

Reinforcement learning can acquire complex behaviors from high-level spe...

Learning Trajectory Prediction with Continuous Inverse Optimal Control via Langevin Sampling of Energy-Based Models

Autonomous driving is a challenging multiagent domain which requires opt...

Learning to Optimize via Wasserstein Deep Inverse Optimal Control

We study the inverse optimal control problem in social sciences: we aim ...

Hyperspectral Image Recovery via Hybrid Regularization

Natural images tend to mostly consist of smooth regions with individual ...

On Forward Kinematics of a 3SPR Parallel Manipulator

In this paper, a new numerical method to solve the forward kinematics (F...

Please sign up or login with your details

Forgot password? Click here to reset