Source note
Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation
Summary
Move-Then-Operate is a vision-language-action policy that splits robot manipulation into two phases: moving near the target and then doing the precise contact work. On RoboTwin2, this phased design beats a monolithic pi_0 policy by a large margin and reaches strong results with less data and fewer training steps.
Problem
- Standard VLA manipulation policies learn coarse reaching and fine contact control with one policy, even though these behaviors have different action scales and dynamics.
- The paper argues that move segments dominate the data and gradients, which makes it harder to learn the smaller, contact-critical operate actions.
- This matters for high-precision manipulation because failures often happen in the final contact stage, where small action errors can break the task.
Approach
- The method introduces a dual-expert policy: one expert for move and one for operate. A lightweight router picks one expert per action chunk.
- Both experts use Conditional Flow Matching to generate actions, but their parameters are separate, so coarse motion and precise manipulation do not share the same low-level action head.
- During training, the router is supervised with phase labels, and each example updates only the matching expert. That keeps gradient updates phase-specific.
- Phase labels are auto-generated with an MLLM-based segmentation pipeline that uses video, language instruction, subtask decomposition, and cues such as end-effector velocity.
- The model is built on a pre-trained pi_0-base backbone with separate LoRA adapters for the two experts and the shared vision-language backbone.
Results
- On 8 RoboTwin2 tasks with 50 demonstrations per task and identical training steps, the method reaches 68.88% average success, versus 44.75% for pi_0, 35.63% for RDT, and 31.63% for ACT. That is +24.13 points over pi_0.
- Per-task gains over pi_0 include: Click Alarmclock 88% vs 63% (+25), Click Bell 99% vs 44% (+55), Move Pillbottle pad 37% vs 21% (+16), Place Bread Basket 34% vs 17% (+17), Place Cans Plasticbox 79% vs 34% (+45), Place Empty Cup 55% vs 37% (+18), Place Burger Fries 89% vs 80% (+9), Press Stapler 70% vs 62% (+8).
- Against data-rich baselines trained with 10x more data, the method is competitive on several tasks: Click Bell 99% vs 75% for pi_0.5* and 98% for GO-1*, and Press Stapler 93% vs 80% and 66%.
- The abstract claims the model reaches peak performance in 40% fewer training steps than the standard full training budget.
- The excerpt gives concrete numbers for benchmark success rates and selected baseline comparisons, but it does not include full aggregate averages for the 10x-data comparison table or the detailed training-efficiency curve values.