# LoLA: Long Horizon Latent Action Learning for General Robot Manipulation

arXiv: https://arxiv.org/abs/2512.20166 · HTML: https://mlsanity.com/paper/2512.20166/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2025.

## Built on by (most cited first, 9 of 9)

- [The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook](https://mlsanity.com/paper/2604.02029/) (2026)
- [EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies](https://mlsanity.com/paper/2606.20092/) (2026)
- [MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models](https://mlsanity.com/paper/2606.09827/) (2026)
- [μVLA: On Recurrent Memory for Partially Observable Manipulation in VLA Models](https://mlsanity.com/paper/2606.12497/) (2026)
- [Recursive Belief Vision Language Action Models](https://mlsanity.com/paper/2602.20659/) (2026)
- [PAMAE: Phase-Aware-MoE Action Experts Towards Reliable Flow-Matching Vision-Language-Action Policies](https://mlsanity.com/paper/2606.27144/) (2026)
- [How Should Vision-Language-Action Models Use Proprioceptive State?](https://mlsanity.com/paper/2608.03052/) (2026)
- [PAVE: Predictive Alignment and Value-Guided Evolution for World-Action Policies](https://mlsanity.com/paper/2608.30378/) (2026)
- [SCULPT-VLA: Learning Structured Control through Staged Action Grounding](https://mlsanity.com/paper/2609.23275/) (2026)

## Abstract (arXiv)

The capability of performing long-horizon, language-guided robotic manipulation tasks critically relies on leveraging historical information and generating coherent action sequences. However, such capabilities are often overlooked by existing Vision-Language-Action (VLA) models. To solve this challenge, we propose LoLA (Long Horizon Latent Action Learning), a framework designed for robot manipulation that integrates long-term multi-view observations and robot proprioception to enable multi-step reasoning and action generation. We first employ Vision-Language Models to encode rich contextual features from historical sequences and multi-view observations. We further introduces a key module, State-Aware Latent Re-representation, which transforms visual inputs and language commands into actionable robot motion space. Unlike existing VLA approaches that merely concatenate robot proprioception (e.g., joint angles) with VL embeddings, this module leverages such robot states to explicitly ground VL representations in physical scale through a learnable "embodiment-anchored" latent space. We trained LoLA on diverse robotic pre-training datasets and conducted extensive evaluations on simulation benchmarks (SIMPLER and LIBERO), as well as two real-world tasks on Franka and Bi-Manual Aloha robots. Results show that LoLA significantly outperforms prior state-of-the-art methods (e.g., pi0), particularly in long-horizon manipulation tasks.