Constrained robot motion planning is a ubiquitous need for robots interacting with everyday environments, but it is a notoriously difficult problem to solve. Many sampled points in a sample-based planner need to be rejected as they fall outside the constraint manifold, or require significant iterative effort to correct. Given this, few solutions exist that present a constraint-satisfying trajectory for robots, in reasonable time and of low path cost. In this work, we present a transformer-based model for motion planning with task space constraints for manipulation systems. Vector Quantized-Motion Planning Transformer (VQ-MPT) is a recent learning-based model that reduces the search space for unconstrained planning for sampling-based motion planners. We propose to adapt a pre-trained VQ-MPT model to reduce the search space for constraint planning without retraining or finetuning the model. We also propose to update the neural network output to move sampling regions closer to the constraint manifold. Our experiments show how VQ-MPT improves planning times and accuracy compared to traditional planners in simulated and real-world environments. Unlike previous learning methods, which require task-related data, our method uses pre-trained neural network models and requires no additional data for training and finetuning the model making this a \textit{one-shot} process. We also tested our method on a physical Franka Panda robot with real-world sensor data, demonstrating the generalizability of our algorithm. We anticipate this approach to be an accessible and broadly useful for transferring learned neural planners to various robotic-environment interaction scenarios.

翻译：约束机器人运动规划是机器人与日常环境交互中的普遍需求，但该问题以难以求解著称。在基于采样的规划器中，大量采样点因落在约束流形之外而需被拒绝，或需大量迭代修正。因此，能在合理时间内生成低路径代价的约束满足轨迹的解决方案寥寥无几。本文提出一种基于变换器的模型，用于操控系统的任务空间约束运动规划。向量量化运动规划变换器（VQ-MPT）是一种近期提出的基于学习的模型，可缩减无约束规划中基于采样的运动规划器的搜索空间。我们提出通过适配预训练的VQ-MPT模型来缩减约束规划的搜索空间，且无需重新训练或微调模型。同时，我们提出更新神经网络输出，使采样区域更接近约束流形。实验表明，在模拟与真实环境中，相比传统规划器，VQ-MPT能提升规划时间与精度。与需要任务相关数据的先前学习方法不同，本方法利用预训练神经网络模型，无需额外数据进行训练与微调，实现"一次性"流程。我们还使用真实传感器数据在实体Franka Panda机器人上测试该方法，验证了算法的泛化能力。我们预期该方法易于采用，并能广泛适用于将学习的神经规划器迁移至各类机器人-环境交互场景。