aiqbench analysis
This research presents a self-supervised learning framework for robotic trajectory planning that utilizes forward and inverse models to navigate obstacle-rich environments more efficiently than traditional sampling-based methods. The study addresses critical limitations in sample efficiency and generalization by proposing specific training regimes to mitigate the tendency of neural planners to over-exploit internal model signals. By leveraging neuro-inspired architectures, this work advances the practical deployment of AI-driven robotics capable of autonomous collision avoidance without relying on extensive expert demonstrations.
Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance
arXiv:2607.20743v1 Announce Type: cross 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.
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.
12 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
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2607.20743 [cs.RO]
(or arXiv:2607.20743v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.20743 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 207 words
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