Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance
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-...
| Authors | Miroslav Krupa, Miroslav Cibula, Kristína Malinovská |
|---|---|
| Primary category | cs.RO |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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