2026-07-24 04:00 UTCOriginal source2 min readUpdated: 2026-07-24 08:47 UTC

Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance

This work tests a neuro-inspired self-supervised learning framework for trajectory planning that uses forward and inverse models as internal supervision. Experiments show feasibility but reveal a tendency to exploit the learning signal, leading to proposed mitigation strategies.

SourcearXiv RoboticsAuthor: Miroslav Krupa, Miroslav Cibula, Krist\'ina Malinovsk\'a

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Key points

Trajectory planning is fundamental in robotics; sampling-based methods are computationally expensive in high-dimensional spaces.
A neuro-inspired self-supervised framework using forward and inverse models as internal supervision is proposed.
Experiments confirm feasibility but show the planner tends to exploit the learning signal, requiring additional training regimes.
The paper will be presented at the 2026 International Conference on Artificial Neural Networks (ICANN).

Why it matters

This matters because trajectory planning is fundamental in robotics; sampling-based methods are computationally expensive in high-dimensional spaces.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

This panel is AI-generated and reviewed for accuracy.

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[Submitted on 22 Jul 2026]

Title:Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance

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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.

Comments: 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

Subjects:

Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as: 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)

Submission history

From: Miroslav Cibula [view email] [v1] Wed, 22 Jul 2026 21:47:56 UTC (174 KB)

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