Space utilization and operational time are inherently conflicting in robotic bin packing:
- Achieving denser packing often requires extra reorientations and longer actions.
- Minimizing execution time, in contrast, can lead to wasted bin space.
STEP resolves this conflict through a preference vector, which defines how much weight to place on each objective. By tuning this vector, the policy can prioritize space efficiency, time efficiency, or a balance of both within a single framework.
The Pareto front below illustrates the achievable trade-offs. STEP-n denotes a policy with n items in the buffer in the semi-online setting. The figure shows results for buffers of size 1, 3, and 5, each evaluated across preference vectors ranging from 0 to 1 for both objectives. Larger buffers provide more candidate choices and improve space utilization, while the preference vector governs how the trade-off between space and time is considered, respectively.