Trajectory Optimization for Self-Wrap-Aware Cable-Towed Planar Object Manipulation under Implicit Tension Constraints
This work addresses a specific challenge in deformable-object manipulation for robotics, focusing on cable-towed systems where self-wrapping affects force transmission, and it is incremental by building on existing trajectory optimization methods with novel relaxations.
The paper tackled the problem of cable-towed manipulation where self-wrapping of the cable around object edges changes force transmission, by formulating it as a trajectory optimization problem with implicit tension constraints and routing-aware transmission maps. The result showed that their Implicit-Mode Relaxation method induced self-wrap through state evolution and exploited redirected torque channels, outperforming more conservative explicit routing approaches in planar towing tasks.
Cable/rope elements are pervasive in deformable-object manipulation, often serving as a deformable force-transmission medium whose routing and contact determine how wrenches are delivered. In cable-towed manipulation, transmission is unilateral and hybrid: the tether can pull only when taut and becomes force-free when slack; in practice, the tether may also contact the object boundary and self-wrap around edges, which is not merely collision avoidance but a change of the wrench transmission channel by shifting the effective application point and moment arm, thereby coupling routing geometry with rigid-body motion and tensioning. We formulate self-wrap towing as a routing-aware, tensioning-implicit trajectory optimization (TITO) problem that couples (i) a tensioning-implicit taut/slack constraint and (ii) routing-conditioned transmission maps for effective length and wrench, and we build a relaxation hierarchy from a strict mode-conditioned reference to three tractable relaxations: Full-Mode Relaxation (FMR), Binary-Mode Relaxation (BMR), and Implicit-Mode Relaxation (IMR). Across planar towing tasks, we find that making routing an explicit decision often yields conservative solutions that stay near switching boundaries, whereas IMR induces self-wrap through state evolution and exploits the redirected torque channel whenever turning requires it.