Understanding Asynchronous Inference Methods for Vision-Language-Action Models
Vision-Language-Action (VLA) models offer a promising path to generalist robot control, but their inference latency causes observation staleness when generated actions are executed asynchronously. Several methods have been proposed concurrently to mitigate this problem: inference-time inpainting (IT-RTC), training-time delay simulation (TT-RTC), future-state-aware conditioning (VLASH), and lightweight residual correction (A2C2). Each takes a fundamentally different approach, but they have so far been evaluated independently with different codebases, base policies, and protocols. We present a systematic comparison of these four methods under controlled conditions. We develop two unified co...
| Authors | Ayoub Agouzoul |
|---|---|
| Primary category | cs.RO |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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