DRIFT: Diffusion-based Rule-Inferred For Trajectories

• URL: http://arxiv.org/abs/2603.00936v1
• Date: Sun, 01 Mar 2026 05:54:38 GMT
• Title: DRIFT: Diffusion-based Rule-Inferred For Trajectories
• Abstract summary: Trajectory generation for mobile robots in unstructured environments faces a critical dilemma: balancing smoothness for safe execution with terminal precision for fine-grained tasks.<n>This article proposes DRIFT, a conditional framework designed to generate high-fidelity reference trajectories by integrating two complementary inductive biases.<n>Results demonstrate that DRIFT reconciles these conflicting objectives, achieving centimeter-level imitation fidelity (0.041m FDE) and competitive smoothness (27.19 Jerk)
• Score: 15.78457153766977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory generation for mobile robots in unstructured environments faces a critical dilemma: balancing kinematic smoothness for safe execution with terminal precision for fine-grained tasks. Existing generative planners often struggle with this trade-off, yielding either smooth but imprecise paths or geometrically accurate but erratic motions. To address the aforementioned shortcomings, this article proposes DRIFT (Diffusion-based Rule-Inferred for Trajectories), a conditional diffusion framework designed to generate high-fidelity reference trajectories by integrating two complementary inductive biases. First, a Relational Inductive Bias, realized via a GNN-based Structured Scene Perception (SSP) module, encodes global topological constraints to ensure holistic smoothness. Second, a Temporal Attention Bias, implemented through a novel Graph-Conditioned Time-Aware GRU (GTGRU), dynamically attends to sparse obstacles and targets for precise local maneuvering. In the end, quantitative results demonstrate that DRIFT reconciles these conflicting objectives, achieving centimeter-level imitation fidelity (0.041m FDE) and competitive smoothness (27.19 Jerk). This balance yields highly executable reference plans for downstream control.

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