  This work introduces Mamba Imitation Learning (MaIL), a novel imitation
learning (IL) architecture that offers a computationally efficient alternative
to state-of-the-art (SoTA) Transformer policies. Transformer-based policies
have achieved remarkable results due to their ability in handling
human-recorded data with inherently non-Markovian behavior. However, their high
performance comes with the drawback of large models that complicate effective
training. While state space models (SSMs) have been known for their efficiency,
they were not able to match the performance of Transformers. Mamba
significantly improves the performance of SSMs and rivals against Transformers,
positioning it as an appealing alternative for IL policies. MaIL leverages
Mamba as a backbone and introduces a formalism that allows using Mamba in the
encoder-decoder structure. This formalism makes it a versatile architecture
that can be used as a standalone policy or as part of a more advanced
architecture, such as a diffuser in the diffusion process. Extensive
evaluations on the LIBERO IL benchmark and three real robot experiments show
that MaIL: i) outperforms Transformers in all LIBERO tasks, ii) achieves good
performance even with small datasets, iii) is able to effectively process
multi-modal sensory inputs, iv) is more robust to input noise compared to
Transformers.
