Foundation models pre-trained on web-scale data are shown to encapsulate extensive
world knowledge beneficial for robotic manipulation in the form of task planning. However …

Improving the generalization capabilities of general-purpose robotic agents has long been a
significant challenge actively pursued by research communities. Existing approaches often …

Recent advances in vision-language models (VLMs) have led to improved performance on
tasks such as visual question answering and image captioning. Consequently, these models …

We propose a novel framework for learning high-level cognitive capabilities in robot
manipulation tasks, such as making a smiley face using building blocks. These tasks often …

We demonstrate how a sampling-based robotic planner can be augmented to learn to
understand a sequence of natural language commands in a continuous configuration space …

We propose an autonomous grasping pipeline that relies on geometric information extracted
from segmented point cloud data. This is in contrast to many recent approaches leveraging …

Automated task planning algorithms have been developed to help robots complete complex
tasks that require multiple actions. Most of those algorithms have been developed for" …

Effective planning of long-horizon deformable object manipulation requires suitable
abstractions at both the spatial and temporal levels. Previous methods typically either focus …

We present a framework for solving long-horizon planning problems involving manipulation
of rigid objects that operates directly from a point-cloud observation. Our method plans in the …

We present a visually grounded hierarchical planning algorithm for long-horizon
manipulation tasks. Our algorithm offers a joint framework of neuro-symbolic task planning …