HiF-VLA: Hindsight, Insight and Foresight through Motion Representation for Vision-Language-Action Models
Minghui Lin, Pengxiang Ding, Shu Wang, Zifeng Zhuang, Yang Liu, Xinyang Tong, Wenxuan Song, Shangke Lyu, Siteng Huang, Donglin Wang

TL;DR
HiF-VLA introduces a motion-centric world model for vision-language-action tasks, enabling robots to reason about past and future dynamics for improved long-horizon manipulation.
Contribution
It presents a unified framework leveraging motion for bidirectional temporal reasoning, enhancing long-horizon robotic manipulation performance.
Findings
Surpasses strong baselines on LIBERO-Long and CALVIN ABC-D benchmarks.
Achieves real-world improvements in long-horizon manipulation tasks.
Incur negligible additional inference latency.
Abstract
Vision-Language-Action (VLA) models have recently enabled robotic manipulation by grounding visual and linguistic cues into actions. However, most VLAs assume the Markov property, relying only on the current observation and thus suffering from temporal myopia that degrades long-horizon coherence. In this work, we view motion as a more compact and informative representation of temporal context and world dynamics, capturing inter-state changes while filtering static pixel-level noise. From this perspective, HiF-VLA equips a motion-centric world model for the VLA, enabling agents to reason about temporal dynamics for future evolution during action generation. Building on this idea, we propose HiF-VLA (Hindsight, Insight, and Foresight for VLAs), a unified framework that leverages motion for bidirectional temporal reasoning. HiF-VLA encodes past dynamics through hindsight priors,…
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