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
2025-12-11
Summary
This paper introduces a new approach to help robots perform complex tasks over a longer period of time by better understanding what's happening around them and planning ahead.
What's the problem?
Current robots using vision and language to understand instructions often struggle with tasks that require remembering past steps or planning for future actions. They tend to focus only on what they see *right now*, which is like having short-term memory and makes it hard to complete tasks that take many steps, because they can't keep track of the bigger picture or anticipate what needs to happen next.
What's the solution?
The researchers developed a system called HiF-VLA that helps robots 'think while acting'. Instead of just reacting to the current view, it uses information about *how things are moving* to understand the past, predict the future, and make better decisions. It's like the robot is looking back at what it's already done, imagining what will happen next, and then using both to guide its current actions. This system combines 'hindsight' – learning from past movements, 'foresight' – predicting future movements, and a way to put them together for smarter action.
Why it matters?
This work is important because it allows robots to perform more complex and realistic tasks in the real world. By overcoming the 'short-term memory' problem, robots can now handle tasks that require planning and remembering, making them more useful in everyday situations and potentially opening up new possibilities for automation.
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. 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.