ForceDelta-VLA: Distilling Force-Conditioned ActionCorrections for Contact-Rich Manipulation

2026-09-16

AI Summary

为了解决接触丰富操作中任务级运动和接触调整结合的问题，提出ForceDelta-VLA方法，通过从力条件模式和无力量模式的预测中提取校正目标来改进动作预测。

Abstract

Force-aware Vision-Language-Action (VLA) policies improve contact-rich manipulation, but typically combine task-level motion and contact-dependent adjustment in a single action prediction. Demonstrations provide no explicit labels for decomposing that prediction into a reusable reference action and a correction. We present ForceDelta-VLA, a correction-distillation framework that constructs an explicit force-correction target using paired predictions from a frozen teacher's force-conditioned and learned force-agnostic modes. A separate delay-correction target accounts for reference-action mismatch and the change in reference state. Training uses asynchronous schedule replay with the cached task context available during execution. The resulting lightweight policy adjusts the reference actions using recent force history and robot state, responding to contact changes between reference-action updates without regenerating complete action chunks. Across nine single-arm and bimanual contact-rich tasks, ForceDelta-VLA achieves an 82.2% mean success rate, compared with 54.4% for the original ForceVLA baseline. Direct execution of our Stage-1 Temporal Teacher achieves 70.6%. Relative to ForceVLA, the complete system reduces mean peak contact force over successful trials by approximately 26% on both platforms.

Problem

• contact-rich manipulation
• force-aware VLA policies
• action prediction

Innovation

• force-correction target
• delay-correction target
• asynchronous schedule replay
• contact-rich manipulation
• reference-action update