Conceitos essenciais
Developing a Personalized Grasping Agent (PGA) through single human-robot interaction enhances object comprehension and grasping efficiency.
Resumo
The article introduces the concept of Language-Conditioned Robotic Grasping (LCRG) and presents a novel task scenario called GraspMine. The focus is on developing a Personalized Grasping Agent (PGA) that learns to grasp personal objects based on natural language instructions through minimal human-robot interaction. PGA leverages unlabeled image data from the user's environment, termed Reminiscence, to adapt its object grounding model efficiently. The proposed method outperforms baseline models in offline experiments and showcases real-world applicability through physical robot execution. Key highlights include:
- Introduction of Language-Conditioned Robotic Grasping (LCRG)
- Novel task scenario: GraspMine for personalized object grasping
- Development of Personalized Grasping Agent (PGA)
- Leveraging Reminiscence for efficient learning and adaptation
- Performance comparison with baselines in offline experiments
- Real-world applicability demonstrated through physical robot execution
Estatísticas
"PGA adapts the object grounding model to grasp personal objects."
"PGA outperforms baseline methods across all metrics."
"Supervised model trained with nearly 9k annotated samples."
Citações
"Empowering robots with the ability to comprehend human natural language presents a formidable yet vital challenge within the realms of AI and Robotics."
"Our proposed method, Personalized Grasping Agent (PGA), addresses GraspMine by leveraging the unlabeled image data of the user’s environment, called Reminiscence."
"PGA adapts the object grounding model to grasp personal objects."