🤖 AI Summary
This work addresses the challenges of high computational cost, low sample efficiency, and poor generalization in robotic trajectory planning within high-dimensional, cluttered environments. To overcome these limitations, we propose a neuro-inspired self-supervised learning framework that jointly trains forward and inverse dynamics models, leveraging intrinsic supervisory signals instead of expert demonstrations or extensive environmental exploration. A novel training strategy is introduced to mitigate over-reliance on learned signals, enhancing model robustness. Experimental results demonstrate that the proposed approach significantly improves planning efficiency, performance, and robustness in complex obstacle-rich scenarios, enabling effective and generalizable trajectory generation.
📝 Abstract
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.