Bio-inspired self-supervised method for robot obstacle avoidance

Trajectory planning remains a core robotics challenge, often tackled by sampling-based methods that are computationally heavy in high-dimensional or cluttered spaces. Model-based learning offers a lighter alternative but typically requires either costly exploration or expert demonstrations. In a new preprint headed for ICANN 2026, researchers Miroslav Krupa, Miroslav Cibula, and Kristína Malinovská present a neuro-inspired self-supervised framework that sidesteps these issues. The system leverages forward and inverse neural models to generate collision-free paths without ground-truth labels, using the models themselves as an internal supervisory signal.

In experiments with static obstacles, the approach proved feasible but revealed a tendency to exploit the supervisory signal, leading to suboptimal trajectories. To counter this, the team proposes additional training regimes and mitigation strategies, though full comparative performance numbers are not yet detailed. The work is supported by the Slovak Research and Development Agency (APVV-21-0105) and demonstrates a promising path toward sample-efficient, generalizable robot path planning. If these exploitation issues can be resolved, the method could enable robots to learn navigation in unfamiliar environments without the need for exhaustive offline simulations or human annotations.