Typical end-to-end formulations for learning robotic navigation involve predicting a small set
of steering command actions (eg, step forward, turn left, turn right, etc.) from images of the …

Abstract This paper introduces Hierarchical Diffusion Policy (HDP) a hierarchical agent for
multi-task robotic manipulation. HDP factorises a manipulation policy into a hierarchical …

Intelligent manipulation benefits from the capacity to flexibly control an end-effector with high
degrees of freedom (DoF) and dynamically react to the environment. However, due to the …

BACKGROUND Humans have a fantastic ability to manipulate objects of various shapes,
sizes, and materials and can control the objects' position in confined spaces with the …

Robots have to face challenging perceptual settings, including changes in viewpoint,
lighting, and background. Current simulated reinforcement learning (RL) benchmarks such …

Mobile manipulation has a broad range of applications in robotics. However, it is usually
more challenging than fixed-base manipulation due to the complex coordination of a mobile …

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 …

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 …

Imitating human demonstrations is a promising approach to endow robots with various
manipulation capabilities. While recent advances have been made in imitation learning and …

We introduce the Universal Manipulation Policy Network (UMPNet)–a single image-based
policy network that infers closed-loop action sequences for manipulating articulated objects …