Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

The grand aim of having a single robot that can manipulate arbitrary objects in diverse
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …

Abstract 3D vision-language (3D-VL) grounding, which aims to align language with 3D
physical environments, stands as a cornerstone in developing embodied agents. In …

Building general-purpose robots that operate seamlessly in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied
agents. The core concept—reusing prior knowledge to learn in and from novel situations—is …

Recent advances in robot learning have shown promise in enabling robots to perform a
variety of manipulation tasks and generalize to novel scenarios. One of the key contributing …

Prompt-based learning has emerged as a successful paradigm in natural language
processing, where a single general-purpose language model can be instructed to perform …

With recent developments in Embodied Artificial Intelligence (EAI) research there has been
a growing demand for high-quality large-scale interactive scene generation. While prior …

Policies often fail at test-time due to distribution shifts—changes in the state and reward that
occur when an end user deploys the policy in environments different from those seen in …

Real-time path planning in outdoor environments still challenges modern robotic systems
due to differences in terrain traversability, diverse obstacles, and the necessity for fast …