HiF-VLA: Hindsight, Insight and Foresight through Motion Representation for Vision-Language-Action Models

arXiv cs.RO / 4/10/2026

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Key Points

The paper argues that Vision-Language-Action (VLA) models often suffer from “temporal myopia” by assuming the Markov property and only using the current observation for long-horizon tasks.
HiF-VLA introduces motion as a compact, informative representation of temporal context and world dynamics, filtering static pixel noise while capturing inter-state changes.
The proposed framework performs bidirectional temporal reasoning using hindsight (past dynamics), insight (integrated past context), and foresight (future evolution) during action generation.
HiF-VLA uses a hindsight-modulated joint expert to support a “think-while-acting” paradigm, improving long-horizon manipulation coherence.
Experiments show performance gains over strong baselines on LIBERO-Long and CALVIN ABC-D benchmarks and also in real-world long-horizon manipulation, with negligible extra 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, anticipates future motion via foresight reasoning, and integrates both through a hindsight-modulated joint expert to enable a ''think-while-acting'' paradigm for long-horizon manipulation. As a result, HiF-VLA surpasses strong baselines on LIBERO-Long and CALVIN ABC-D benchmarks, while incurring negligible additional inference latency. Furthermore, HiF-VLA achieves substantial improvements in real-world long-horizon manipulation tasks, demonstrating its broad effectiveness in practical robotic settings.