Drones as advanced cyber-physical systems are undergoing a transformative shift with the
advent of vision-based learning, a field that is rapidly gaining prominence due to its …

Data-driven neural path planners are attracting increasing interest in the robotics
community. However, their neural network components typically come as black boxes …

Learning a generalist embodied agent capable of completing multiple tasks poses
challenges, primarily stemming from the scarcity of action-labeled robotic datasets. In …

Acquiring a multi-task imitation policy in 3D manipulation poses challenges in terms of
scene understanding and action prediction. Current methods employ both 3D representation …

This work introduces the Multimodal Diffusion Transformer (MDT), a novel diffusion policy
framework, that excels at learning versatile behavior from multimodal goal specifications …

Encouraged by the remarkable achievements of language and vision foundation models,
developing generalist robotic agents through imitation learning, using large demonstration …

A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is
more severe in mobile manipulation, where collecting demonstrations is harder than in …

The dominant paradigm for end-to-end robot learning focuses on optimizing task-specific
objectives that solve a single robotic problem such as picking up an object or reaching a …

In the field of robotic manipulation, deep imitation learning is recognized as a promising
approach for acquiring manipulation skills. Additionally, learning from diverse robot datasets …

This paper aims to enable multi-agent systems to effectively utilize past memories to adapt to
novel collaborative tasks in a data-efficient fashion. We propose the Multi-Agent …