Abstract
Coordinating whole-body shape generation, geometric object caging, and collision-aware transportation remains challenging for hyper-redundant robots. This study presents a framework integrating Transformer-based backbone prediction, cage-preserving geometric refinement, and neural shape control for a planar 50-link robot operating among static and moving obstacles. The framework was evaluated in five simulated scenes under idealized kinematic assumptions without explicit actuator dynamics, contact forces, or sensing uncertainty. The full implementation achieved a geometric cage success rate of 100%, with mean cage-center and cage-radial errors of 0.001786 and 0.000062, respectively. However, the mean robot–obstacle and object–obstacle collision rates were 75.22% and 15.87%, and none of the five scenes satisfied the complete task-success criterion. A controlled ablation of soft cage attraction produced nearly unchanged performance when hard cage projection was retained, indicating limited incremental benefit from the soft attraction term under the evaluated conditions. This experiment does not isolate the contribution of symmetry preservation as a whole. A dedicated CPU benchmark measured a mean control-cycle latency of 128.792 ± 2.401 ms, with shape optimization accounting for approximately 51.4% of the mean cycle time. The findings demonstrate consistent maintenance of local enclosure geometry but do not establish collision-free transportation or physically feasible manipulation. Further development should prioritize whole-body feasibility, collision-aware motion planning, and validation across additional training seeds and physically realistic environments.