Understanding Multimodality in Generative Behavioral Cloning
Analysis shows generative behavioral cloning faces distinct bottlenecks when one observation has several valid actions.
Behavioral cloning becomes difficult when the same observation admits several valid actions. The paper analyzes how generative policies represent multimodal expert behavior and identifies different bottlenecks: latent-variable policies need action-conditioned latent information that excessive posterior-prior regularization can suppress, while action-space generators are limited by the smoothness of the base-to-action transport. Experiments on synthetic multimodal navigation and a physical-robot bimodal manipulation task support these mechanisms. Standard robotic simulation benchmarks showed limited conditional multimodality, where deterministic regression remained competitive.
- Behavioral cloning is harder when one observation admits several valid actions.
- Strong posterior-prior regularization can erase demonstrated mode information in latents.
- Smooth base-to-action maps cannot cover many well-separated action modes.
- Synthetic navigation and a bimodal robot manipulation task support the analysis.
- Standard simulation benchmarks show limited multimodality, so regression stays competitive.
From the original article
1 min read at huggingface.co
Behavioral cloning becomes challenging when the same observation admits several valid actions. We study how generative behavioral-cloning policies represent such multimodal expert behavior and identify different bottlenecks across model parameterizations. For latent-variable policies, preserving demonstrated modes requires action-conditioned information in the latent representation.
By Lorenzo Mazza, Massimiliano Datres, Ariel Rodriguez · huggingface.co
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