Image-generation diffusion models have been fine-tuned to unlock new capabilities such as
image-editing and novel view synthesis. Can we similarly unlock image-generation models …

Generalising vision-based manipulation policies to novel environments remains a
challenging area with limited exploration. Current practices involve collecting data in one …

Learning from demonstrations faces challenges in generalizing beyond the training data
and is fragile even to slight visual variations. To tackle this problem, we introduce Lan-o3dp …

We introduce BiGym, a new benchmark and learning environment for mobile bi-manual
demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home …

Learning from expert demonstrations is a promising approach for training robotic
manipulation policies from limited data. However, imitation learning algorithms require a …

The increasing complexity of tasks in robotics demands efficient strategies for multitask and
continual learning. Traditional models typically rely on a universal policy for all tasks, facing …

Recently, driven by advancements in Multimodal Large Language Models (MLLMs), Vision
Language Action Models (VLAMs) are being proposed to achieve better performance in …

Generalizable long-horizon robotic assembly requires reasoning at multiple levels of
abstraction. End-to-end imitation learning (IL) has been proven a promising approach, but it …

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 …

In the field of Robot Learning, the complex mapping between high-dimensional
observations such as RGB images and low-level robotic actions, two inherently very different …