Modern tools for class-agnostic image segmentation (eg, SegmentAnything) and open-set
semantic understanding (eg, CLIP) provide unprecedented opportunities for robot …

The realization of universal robots is an ultimate goal of researchers. However, a key hurdle
in achieving this goal lies in the robots' ability to manipulate objects in their unstructured …

This paper presents the Embedding Pose Graph (EPG), an innovative method that combines
the strengths of foundation models with a simple 3D representation suitable for robotics …

Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

Large-scale robotic policies trained on data from diverse tasks and robotic platforms hold
great promise for enabling general-purpose robots; however, reliable generalization to new …

This paper introduces CLIPSwarm, a new algorithm designed to automate the modeling of
swarm drone formations based on natural language. The algorithm begins by enriching a …

Reinforcement Learning (RL) provides a framework in which agents can be trained, via trial
and error, to solve complex decision-making problems. Learning with little supervision …

Precise manipulation that is generalizable across scenes and objects remains a persistent
challenge in robotics. Current approaches for this task heavily depend on having a …

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 …

Enabling robots to follow complex natural language instructions is an important yet
challenging problem. People want to flexibly express constraints, refer to arbitrary landmarks …