用自监督神经机制实现高效避障轨迹规划
Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance
- 基于前向与逆向模型构建自监督学习框架
- 在有障碍环境中验证可行性，但存在信号滥用倾向
- 提出新训练策略提升规划可靠性，适合机器人路径规划研究者
轨迹规划是机器人学中的基础问题，需在复杂环境中生成无碰撞且高效的运动路径。尽管采样方法仍是主流，但在高维空间和障碍物密集环境下计算成本高昂。基于模型学习的方法通过少量神经网络前向传播实现高效规划，但常因依赖探索或专家示范而样本效率低、泛化能力差。本文测试了我们受神经启发的自监督学习框架在含障碍物环境中的轨迹规划性能。实验表明该方法可行，但发现规划器倾向于滥用前向与逆向模型提供的学习信号。为此，本文提出并评估了新的训练方案与缓解策略。
原文摘要 · Abstract (English)
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.
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