RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures
Summary
RoboTALES introduces a two-stage framework combining LLM-based planning and VLM-based criticism to improve task-aligned video generation and robotic policy training, significantly outperforming existing methods on long-horizon manipulation tasks.
View Cached Full Text
Cached at: 07/09/26, 11:41 PM
Paper page - RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures
Source: https://huggingface.co/papers/2607.06018
Abstract
RoboTALES introduces a two-stage framework that combines LLM-based planning and VLM-based criticism to improve task-aligned video generation and robotic policy training.
Pretrainedvideo generative modelsare promising backbones forvisuomotor control, but their imagined futures often drift from task intent and are not reliably action-conditional. As a result, these models can be difficult to use for planning or policy extraction. To address these limitations, we propose RoboTALES, a single-stage framework that learnstask-aligned simulated futuresand uses them to trainrobot policies. Our approach introduces two key innovations: (1) ahierarchical LLM-based plannerthat breaks complex tasks into a sequence of subgoals to guide the model’s imagination; and (2) aVLM-based criticthat evaluates these ``imagined’’ futures and uses reward-based feedback to keep the model’s internal representations focused on the goal. By anchoring the video generator in abstract reasoning, we produce temporally consistent rollouts and more coherent actions. We evaluate RoboTALES on diverse manipulation tasks from RoboCasa and LIBERO10, and show that our method consistently outperforms existing methods, especially inlong-horizon tasks. Our code and models are publicly available at https://github.com/hananshafi/RoboTALES.
View arXiv pageView PDFProject pageGitHub2Add to collection
Get this paper in your agent:
hf papers read 2607\.06018
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper1
#### hanangani/robotales-ckpts Updatedabout 1 hour ago
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2607.06018 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2607.06018 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
RoboLab is a high-fidelity simulation benchmarking framework for evaluating task-generalist robotic policies, introducing the RoboLab-120 benchmark with 120 tasks across visual, procedural, and relational competency axes. It enables scalable, realistic task generation and systematic analysis of policy behavior under controlled perturbations to assess true generalization capabilities.
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
EvoTrainer introduces an autonomous training framework that co-evolves LLM policies and training harnesses through empirical feedback, outperforming human-engineered RL baselines on mathematical reasoning, code generation, and long-horizon software engineering tasks.
SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation
SimFoundry is a modular system that automates real-to-sim scene construction from video, generating digital twins and affordance-preserving variations for zero-shot robot policy training, achieving strong transfer to real-world tasks and high simulation-to-real performance prediction.
RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data
RoboEvolve is a framework that co-evolves a VLM planner and VGM simulator for robotic manipulation, achieving data efficiency with only 500 unlabeled seed images and robust continual learning.
Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization
This paper proposes an exploration-aware reinforcement learning framework that enables LLM agents to adaptively explore only when uncertainty is high, improving performance on text-based and GUI-based benchmarks.