Robot models, particularly those trained with large amounts of data, have recently shown a
plethora of real-world manipulation and navigation capabilities. Several independent efforts …

We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework
including best practices for fine-tuning diffusion-based policies (eg Diffusion Policy) in …

We introduce Latent Action Pretraining for general Action models (LAPA), an unsupervised
method for pretraining Vision-Language-Action (VLA) models without ground-truth robot …

A key challenge in manipulation is learning a policy that can robustly generalize to diverse
visual environments. A promising mechanism for learning robust policies is to leverage …

One of the roadblocks for training generalist robotic models today is heterogeneity. Previous
robot learning methods often collect data to train with one specific embodiment for one task …

Learning from Demonstrations, the field that proposes to learn robot behavior models from
data, is gaining popularity with the emergence of deep generative models. Although the …

Generalization capabilities, or rather a lack thereof, is one of the most important unsolved
problems in the field of robot learning, and while several large scale efforts have set out to …

Motion manifold primitives (MMP), a manifold-based approach for encoding basic motion
skills, can produce diverse trajectories, enabling the system to adapt to unseen constraints …

Recent advances in behavior cloning (BC), like action-chunking and diffusion, have led to
impressive progress. Still, imitation alone remains insufficient for tasks requiring reliable and …

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) …