# RoLD: Robot Latent Diffusion for Multi-task Policy Modeling

arXiv: https://arxiv.org/abs/2403.07312 · HTML: https://mlsanity.com/paper/2403.07312/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 26 of its 31 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022)
- [Learning Universal Policies via Text-Guided Video Generation](https://mlsanity.com/paper/2302.00111/) (2023)
- [VIMA: General Robot Manipulation with Multimodal Prompts](https://mlsanity.com/paper/2210.03094/) (2022)
- [Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?](https://mlsanity.com/paper/2303.18240/) (2023)
- [Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning](https://mlsanity.com/paper/2002.08396/) (2020)
- [What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?](https://mlsanity.com/paper/2307.02469/) (2023)
- [LISA: Learning Interpretable Skill Abstractions from Language](https://mlsanity.com/paper/2203.00054/) (2022)
- [Spatiotemporal Predictive Pre-training for Robotic Motor Control](https://mlsanity.com/paper/2403.05304/) (2024)

## Built on by (most cited first, 3 of 3)

- [Enhancing Large Language Models through Neuro-Symbolic Integration and Ontological Reasoning](https://mlsanity.com/paper/2504.07640/) (2025)
- [GraspLDP: Towards Generalizable Grasping Policy via Latent Diffusion](https://mlsanity.com/paper/2602.22862/) (2026)
- [Ghost Attractor Networks: Basin-Structured Dynamical Decoders for Closed-Loop Sequential Generation](https://mlsanity.com/paper/2606.18315/) (2026)

## Abstract (arXiv)

Modeling generalized robot control policies poses ongoing challenges for language-guided robot manipulation tasks. Existing methods often struggle to efficiently utilize cross-dataset resources or rely on resource-intensive vision-language models, thus limiting their multi-task performance and practical applications. In this study, we propose a novel approach that decouples robot action trajectory encoding and control policy generation by leveraging latent action trajectory spaces, enhancing the generalization ability of policy generation on multi-task manipulation tasks. First, we pre-train a task-agnostic auto-encoder to project an action trajectory of several frames accompanied with observations into a latent action trajectory space on large-scale datasets collected with multiple embodiments in various environments. Then we propose learning a diffusion model based on the latent action trajectory space to generate actions of next steps. Through experiments on two widely used benchmarks, results demonstrate that our proposed method outperforms baselines by 7%-29% in terms of average success rate across eight tasks. Our method can consistently benefit from pre-training while baselines cannot. Our method is more than two times faster than our baseline.