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

Abstract

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/.

Figures (14)

• Figure 1: Deep learning for robotic manipulator motion planning. (a, b) Point cloud networks (PCNets) for End-to-end (E2E) planning fishman2023motiondalal2024neural (Section \ref{['subsec:DL-endtoend']}). (c) Variational autoencoders (VAEs) johnson2023learning, and (d) normalizing flows lai2021plannerflows for informed sampling (Section \ref{['subsec:DL-constrainedsampling']}). (e) Multilayer perceptions (MLPs) for trajectory optimization ichnowski2020deep (Section \ref{['subsec:DL-optimization']}). (f) Convolutional neural networks (CNNs) for E2E planning ni2024physics (Section \ref{['subsec:DL-endtoend']}). (g) Generative adversarial networks (GANs) for constraint manifold learning acar2021approximating (Section \ref{['subsec:DL-constrainedsampling']}). (h) MLPs for trajectory optimization kicki2023fast (Section \ref{['subsec:DL-optimization']}). (i) Graph neural networks (GNNs) for collision checking kim2022graphdistnet (Section \ref{['subsec:DL-collisionchecking']}). (j) MLPs for steering chiang2021fast (Section \ref{['subsubsec: DL-sampling-based-steering']}). (k) VAEs for trajectory optimization osa2022motion (Section \ref{['subsec:DL-optimization']}). (l) CNNs for collision checking danielczuk2021object (Section \ref{['subsec:DL-collisionchecking']}).
• Figure 2: Overview of this survey paper. Figures are adopted from Carvalho et al.carvalho2023motion, Qureshi et al.qureshi2019motion, Bency et al.bency2019neural, and Song et al.song2023graph
• Figure 3: An example of a robotic manipulator's autonomy stack.
• Figure 4: An example of a 2-DOF planar manipulator: workspace vs. configuration spacelynch2017modern.
• Figure 5: Algorithmic primitives (I. Sampling, II. Steering, and III. Collision Checking) of sampling-based planning algorithms.