GAIDE: Graph-based Attention Masking for Spatial- and Embodiment-aware Motion Planning

TL;DR

Graph-based Attention Masking for Spatial- and Embodiment-aware Motion Planning (GAIDE) is introduced, a neural informed sampler that leverages both the spatial structure of the planning problem and the robotic manipulator's embodiment to guide the planning algorithm.

Abstract

Sampling-based motion planning algorithms are widely used for motion planning of robotic manipulators, but they often struggle with sample inefficiency in high-dimensional configuration spaces due to their reliance on uniform or hand-crafted informed sampling primitives. Neural informed samplers address this limitation by learning the sampling distribution from prior planning experience to guide the motion planner towards planning goal. However, existing approaches often struggle to encode the spatial structure inherent in motion planning problems. To address this limitation, we introduce Graph-based Attention Masking for Spatial- and Embodiment-aware Motion Planning (GAIDE), a neural informed sampler that leverages both the spatial structure of the planning problem and the robotic manipulator's embodiment to guide the planning algorithm. GAIDE represents these structures as a graph and integrates it into a transformer-based neural sampler through attention masking. We evaluate GAIDE against baseline state-of-the-art sampling-based planners using uniform sampling, hand-crafted informed sampling, and neural informed sampling primitives. Evaluation results demonstrate that GAIDE improves planning efficiency and success rate.

Figures (7)

• Figure 1: An overview of GAIDE: the proposed neural informed sampler constructs a graph that represents both the robotic manipulator embodiment and the spatial relationships inherent within the motion planning problem. The adjacency matrix of this graph is incorporated into a transformer-based neural sampler via attention masking to enable spatial- and embodiment-aware informed sampling. The framework is embedded within sampling-based planners as an informed sampler and demonstrates superior performance compared to benchmark neural samplers, MPNets qureshi2020motion and SIMPNet soleymanzadeh2025simpnet.
• Figure 2: Graph Construction: an illustration of spatial and embodiment graph. An undirected graph is constructed over the downsampled manipulator point cloud to implicitly encode the manipulator's kinematics chain, while a directed graph connects the downsampled workspace point cloud to the robot nodes to capture the inherent spatial relationships within the motion planning problem. "PCD" denotes point cloud and "PointNet++" is set abstraction layers from PointNet++ qi2017pointnet++.
• Figure 3: GAIDE network architecture: The framework leverages current-time-step workspace information (including the workspace and robot point clouds) together with configuration space features (current-time-step and goal configuration) to generate an informed sample that guides the robotic manipulator toward the motion planning goal. $\mathbf{q}_t,~\mathcal{P}_r$, and $\mathbf{q}_{\text{goal}}$ are the current time-step configuration, current-time step robotic manipulator point cloud, and motion planning goal configuration, respectively. $\mathbf{z}_t$, $\mathbf{z}_{\text{goal}}$, $\mathbf{z}_r$, $\mathbf{z}_w$, and $\delta \mathbf{q}_t$ are the current time-step configuration, planning goal configuration, robot point cloud, scene point cloud embeddings, and predicted joint angle, respectively. "PointNet++" denotes set abstraction layers from PoinNet++ qi2017pointnet++.
• Figure 4: An example of all held-out planning tasks from soleymanzadeh2025perfact.
• Figure 5: Planning cost of GAIDE and baseline planners across all held-out planning tasks.