Universal Planning Networks

TL;DR

Universal Planning Networks (UPN) integrate differentiable planning into goal-directed policies, enabling effective visuomotor control, goal specification, and transfer across different robots through learned representations optimized via imitation learning.

Contribution

Introduction of UPN, a novel framework embedding differentiable planning within policies, improving goal generalization and transfer in visuomotor tasks.

Findings

Effective goal-directed visual imitation via gradient-based planning

Representations enable distance-based rewards for reinforcement learning

Successful transfer of planning strategies across different robot morphologies

Abstract

A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via image-based goals. We were able to achieve successful transfer of visuomotor planning strategies across robots with significantly different morphologies and actuation capabilities.