This paper provides an overview of the current state‐of‐the‐art in selective harvesting robots
(SHRs) and their potential for addressing the challenges of global food production. SHRs …

Robotic manipulation refers to how robots intelligently interact with the objects in their
surroundings, such as grasping and carrying an object from one place to another. Dexterous …

Robots deployed in many real-world settings need to be able to acquire new skills and solve
new tasks over time. Prior works on planning with skills often make assumptions on the …

Articulated objects are common in our daily life. However, current category-level articulation
pose works mostly focus on predicting 9D poses on statistical point cloud observations. In …

Understanding the physical interactions of objects with environments is critical for multi-
object robotic manipulation tasks. A predictive dynamics model can predict the future states …

In this letter, we propose and realize a new deep learning architecture for discovering
symbolic representations for objects and their relations based on the self-supervised …

Humans excel at performing a wide range of sophisticated tasks by leveraging skills
acquired from prior experiences. This characteristic is especially essential in robotics …

Learning object affordances is an effective tool in the field of robot learning. While the data-
driven models investigate affordances of single or paired objects, there is a gap in the …

Interactive exploration of the unknown physical properties of objects such as stiffness, mass,
center of mass, friction coefficient, and shape is crucial for autonomous robotic systems …

Learning object affordances is an effective tool in the field of robot learning. While the data-
driven models delve into the exploration of affordances of single or paired objects, there is a …