A Survey on Robotic Manipulation of Deformable Objects: Recent Advances, Open Challenges and New Frontiers

Deformable object manipulation (DOM) for robots has a wide range of applications in various fields such as industrial, service and health care sectors. However, compared to manipulation of rigid objects, DOM poses significant challenges for robotic perception, modeling and manipulation, due to the infinite dimensionality of the state space of deformable objects (DOs) and the complexity of their dynamics. The development of computer graphics and machine learning has enabled novel techniques for DOM. These techniques, based on data-driven paradigms, can address some of the challenges that analytical approaches of DOM face. However, some existing reviews do not include all aspects of DOM, and some previous reviews do not summarize data-driven approaches adequately. In this article, we survey more than 150 relevant studies (data-driven approaches mainly) and summarize recent advances, open challenges, and new frontiers for aspects of perception, modeling and manipulation for DOs. Particularly, we summarize initial progress made by Large Language Models (LLMs) in robotic manipulation, and indicates some valuable directions for further research. We believe that integrating data-driven approaches and analytical approaches can provide viable solutions to open challenges of DOM.

Robot Learning in the Era of Foundation Models: A Survey

The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning from automation towards general embodied Artificial Intelligence (AI). Adopting foundation models together with traditional learning methods to robot learning has increasingly gained recent interest research community and showed potential for real-life application. However, there are few literatures comprehensively reviewing the relatively new technologies combined with robotics. The purpose of this review is to systematically assess the state-of-the-art foundation model techniques in the robot learning and to identify future potential areas. Specifically, we first summarized the technical evolution of robot learning and identified the necessary preliminary preparations for foundation models including the simulators, datasets, foundation model framework. In addition, we focused on the following four mainstream areas of robot learning including manipulation, navigation, planning, and reasoning and demonstrated how the foundation model techniques can be adopted in the above scenarios. Furthermore, critical issues which are neglected in the current literatures including robot hardware and software decoupling, dynamic data, generalization performance with the presence of human, etc. were discussed. This review highlights the state-of-the-art progress of foundation models in robot learning and future research should focus on multimodal interaction especially dynamics data, exclusive foundation models for robots, and AI alignment, etc.