  We propose enhancing trajectory optimization methods through the
incorporation of two key ideas: variable-grasp pose sampling and trajectory
commitment. Our iterative approach samples multiple grasp poses, increasing the
likelihood of finding a solution while gradually narrowing the optimization
horizon towards the goal region for improved computational efficiency. We
conduct experiments comparing our approach with sampling-based planning and
fixed-goal optimization. In simulated experiments featuring 4 different task
scenes, our approach consistently outperforms baselines by generating
lower-cost trajectories and achieving higher success rates in challenging
constrained and cluttered environments, at the trade-off of longer computation
times. Real-world experiments further validate the superiority of our approach
in generating lower-cost trajectories and exhibiting enhanced robustness. While
we acknowledge the limitations of our experimental design, our proposed
approach holds significant potential for enhancing trajectory optimization
methods and offers a promising solution for achieving consistent and reliable
robotic manipulation.
