Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation

Summary

ACA improves low-budget real-robot VLA adaptation by repeating demonstrations at selected anchor conditions before adding targeted boundary data.

Real-robot demonstrations are costly, so adapting a pretrained VLA to a new robot often has only tens to hundreds of trajectories.
The paper argues that collecting one demonstration for many different conditions can fail under small budgets because each condition has too little data, which leaves high action-estimation noise.
This matters for robot deployment because sparse coverage can make policies unstable in physical tasks with embodiment mismatch and workspace variation.

The paper models adaptation as learning a conditional action vector field and splits error into an estimation term and a coverage term.
With N trajectories and K unique conditions, the simplified bound is Cσ√(K/N) + LcK^(-1/d), so adding more unique conditions reduces coverage error but raises estimation error.
ACA first trains on repeated demonstrations at a small set of workspace anchors to make a stable base policy.
It then runs teacher-forced deviation scoring on probe demonstrations, selects the highest-error boundary conditions, and collects local boundary data.
Stage 2 freezes the Stage-1 policy and trains a LoRA residual branch in the Action Expert, so boundary corrections are added without full-parameter drift.

On a 7-DoF Franka Panda with 4 tabletop tasks, ACA is evaluated on Block Stacking, Cup Placement, Table Cleaning, and Toy Tidying across S@1, S@2, and S@3 regions.
With N=50 trajectories, π0.5 + ACA reaches 46.3% mean success versus 13.8% for π0.5, a +32.5 point gain.
With N=100 trajectories, π0.5 + ACA reaches 72.5% mean success versus 31.7%, a +40.8 point gain.
With N=150 trajectories, π0.5 + ACA reaches 83.8% mean success versus 52.9%, a +30.9 point gain.
The paper reports 20 evaluation rollouts per task-region setting; S@1 covers 25% of the workspace, S@2 covers 50%, and S@3 covers 90%.