Universal Planning Networks (1804.00645v2)

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.

• The paper introduces an end-to-end framework that integrates differentiable planning within a goal-conditioned policy for visuomotor control.
• It demonstrates data-efficient learning by unrolling a forward model in latent space and optimizing via gradient descent.
• The findings show that UPNs generate effective reward signals, enabling improved transferability of visuomotor strategies across tasks and robotics.

Analysis of Universal Planning Networks for Visuomotor Control

The paper "Universal Planning Networks" (UPN) addresses a critical challenge in the domain of visuomotor control: the learning of abstract representations that facilitate effective goal specification, planning, and generalization. The authors introduce a novel planning framework, embedding differentiable planning computations within a neural network, demonstrating its utility in vision-based control tasks. This essay elaborates on the key contributions, findings, and implications of the UPN approach.

Core Contributions

The Universal Planning Network (UPN) integrates a differentiable planner within a goal-conditioned policy, which is optimized end-to-end through a supervised imitation learning objective. The UPN leverages a forward model unrolled in a latent space, inferring an optimal action plan via gradient descent optimization. This architecture provides several notable contributions:

1. Embedded Differentiable Planning: UPN incorporates differentiable planning computations into neural networks, facilitating the learning of representations directly optimizable for planning.
2. Goal Specification Using Images: The learned representations enable goal specification via images by providing a metric for distance-based reward signals.
3. Visuomotor Transfer Capabilities: The approach demonstrates the transferability of visuomotor planning strategies across different robotic morphologies.

Insights and Results

UPNs capitalize on plan-by-gradient-descent processes, allowing the network to handle visually complex control tasks. Significant results of the paper include:

• Efficient Learning: UPNs learn visual imitation policies more data-efficiently than traditional imitation learners, showcasing the inductive bias toward planning.
• Transfer of Representations: The latent representations acquired during training proved effective in facilitating more efficient reinforcement learning (RL) on new tasks with image-based goals. Through the metric-based rewards derived from the UPN's latent space, RL agents display improved learning performance.
• Meta-Learning Evidence: The ability of UPNs to improve performance with additional planning updates at test-time indicates successful meta-learning for planning under limited data regimes.

Importantly, UPNs outperform reactive and autoregressive imitation learner baselines, showcasing the advantage conferred by embedding differentiable gradient-based planning within the policy architecture.

Implications and Future Directions

The findings suggest that UPNs offer a compelling approach to learning transferable and generalizable control policies. By optimizing representations for planning efficacy directly, UPNs avoid the inadequacies of unsupervised representation learning in control tasks. Furthermore, the paper demonstrates the potential of UPNs to act as reward generators in RL scenarios, specifically when extrinsic rewards are difficult to engineer.

For future developments, several avenues could be pursued:

• Self-Supervised Learning Extensions: Investigation of UPN training without manually annotated demonstrations by incorporating self-supervised or RL-based objectives.
• Scaling and Optimization: Explore the incorporation of more sophisticated gradient optimization techniques within the UPN framework.
• Broader Applicability: Testing and adaptation of the UPN approach to real-world robotic applications, potentially bridging the gap between simulation and real-world control.

In conclusion, Universal Planning Networks represent a promising architectural innovation for embedding plan-based learning within neural networks, offering advancements in visuomotor control. The ability to generalize “plannable” representations across tasks and robots opens up exciting possibilities for robust, adaptive, and efficient machine learning systems in robotics and beyond.