Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities

State-of-the-art generalist manipulation policies have enabled the deployment of robotic manipulators in unstructured human environments. However, these frameworks struggle in cluttered environments primarily because they utilize auxiliary modules for low-level motion planning and control. Motion planning remains challenging due to the high dimensionality of the robot’s configuration space and the presence of workspace obstacles. Neural motion planners have enhanced motion planning efficiency by offering fast inference and effectively handling the inherent multi-modality of the motion planning problem. Despite such benefits, current neural motion planners often struggle to generalize to unseen, out-of-distribution planning settings. This paper reviews and analyzes the state-of-the-art neural motion planners, highlighting both their benefits and limitations. It also outlines a path toward establishing generalist neural motion planners capable of handling domain-specific challenges. For a list of the reviewed papers, please refer to https://davoodsz.github.io/planning-manip-survey.github.io/ Note to Practitioners—The paper reviews and summarizes rapidly evolving studies that leverage deep learning for motion planning of robotic manipulators. As robotic manipulators continue to transition from controlled laboratory environments to real-world settings, the demand for efficient, robust, and adaptable motion planning algorithms grows significantly. Thanks to characteristics such as fast inference time and inherent inductive bias, deep learning has been leveraged to facilitate this transition. This paper extensively reviews state-of-the-art deep learning methods used for motion planning of robotic manipulators, and outlines promising avenues and challenges for future research. It specifically evaluates and summarizes the performance of the most commonly used deep learning methods on various key components of motion planning, such as informed sampling, warm-starting trajectory optimization, and collision checking. This paper can serve as a resource for both experts and newcomers in high-DoF robotic motion planning using deep learning.