Exploring the Impact of Latent and Semantic Representation Frameworks on Robotic Grasping and Manipulation of Soft Objects

EasyChair Preprint 13195

Abstract

This research paper investigates the influence of latent and semantic representation frameworks on the efficacy of robotic grasping and manipulation, particularly focusing on soft objects. Leveraging advancements in machine learning and robotics, this study delves into the comparative analysis of latent and semantic representations in guiding robotic actions in the realm of soft object manipulation. Through a series of experiments and simulations, the paper elucidates how different representation frameworks affect the robot's ability to grasp, manipulate, and interact with soft objects in varying environments. Insights gleaned from this exploration provide valuable implications for the development of more adaptable and robust robotic systems tailored for tasks involving soft object manipulation.

Keyphrases: Artificial Intelligence, Latent Representation, Robotic Grasping, Soft object manipulation, control, deformable objects

@booklet{EasyChair:13195,
  author    = {Nakul Mehra and Wahaj Ahmed},
  title     = {Exploring the Impact of Latent and Semantic Representation Frameworks on Robotic Grasping and Manipulation of Soft Objects},
  howpublished = {EasyChair Preprint 13195},
  year      = {EasyChair, 2024}}