发表机构
Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava（布拉迪斯拉发夸美纽斯大学数学、物理与信息学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究机器人轨迹规划问题，提出基于自监督学习框架，利用正反模型作监督机制，在含障碍物环境下规划轨迹，实验证明方法可行，针对规划器问题提出并评估了额外训练机制和缓解策略。
AI 中文摘要
轨迹规划是机器人领域的一个基本问题，需要在潜在复杂环境中生成无碰撞且高效的轨迹。基于采样的规划器虽占主导，但计算成本高。基于模型学习的方法是有前景的替代方案，不过常因依赖探索或专家示范而样本效率低或泛化受限。本文测试了受神经启发的自监督学习框架用于含障碍物环境下的轨迹规划，利用正反模型作为内部监督机制。实验结果证明了该方法的可行性，同时揭示了规划器利用正反模型提供的学习信号的趋势。为此提出并评估了额外的训练机制和缓解策略。
英文摘要
Trajectory planning is a fundamental problem in robotics, requiring the generation of collision-free and efficient trajectories in a potentially complex environment. While sampling-based planners remain the dominant approach, they are often computationally expensive, particularly in high-dimensional spaces and obstacle-rich environments. Methods based on model learning offer a promising alternative, enabling efficient planning through a bounded number of forward passes through a neural trajectory planner, but commonly suffer from low sample efficiency or limited generalisation due to their reliance on exploration or expert demonstrations. This follow-up work tests our neuro-inspired self-supervised learning framework for trajectory planning that leverages forward and inverse models as the internal supervisory mechanism in an environment that contains an obstacle. Experimental results demonstrate the feasibility of the approach while revealing a tendency of our planner to exploit the learning signal provided by the forward and inverse models. To address this issue, additional training regimes and mitigation strategies are proposed and evaluated.
Comments12 pages, 3 figures. To be published in 2026 International Conference on Artificial Neural Networks (ICANN) proceedings. This research was supported by the Slovak Research and Development Agency, project APVV-21-0105
