Video Language Planning
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Cited by 59 in World models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pretrained on Internet-scale data. To this end, we present video language planning (VLP), an algorithm that consists of a tree search procedure, where we train (i) vision-language models to serve as both policies and value functions, and (ii) text-to-video models as dynamics models. VLP takes as input a long-horizon task instruction and current image observation, and outputs a long video plan that provides detailed multimodal (video and language) specifications that describe how to complete the final task. VLP scales with increasing computation budget where more computation time results in improved video plans, and is able to synthesize long-horizon video plans across different robotics domains: from multi-object rearrangement, to multi-camera bi-arm dexterous manipulation. Generated video plans can be translated into real robot actions via goal-conditioned policies, conditioned on each intermediate frame of the generated video. Experiments show that VLP substantially improves long-horizon task success rates compared to prior methods on both simulated and real robots (across 3 hardware platforms).
Key facts. Cited by 162 papers in the map (data through 14 Oct 2026). Builds on 42 of its 52 references that are in the map. First posted on arXiv 16 Oct 2023.
Builds on
42 of 52 references.
References · 42
- 2020Language Models are Few-Shot Learnerscited by 32,419
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- 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardwarecited by 1,568
- 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,556
- 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,210
- 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,081
30 more
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- 2022Compositional Visual Generation with Composable Diffusion Modelscited by 624
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- 2022Socratic Models: Composing Zero-Shot Multimodal Reasoning with Languagecited by 513
- 2022Phenaki: Variable Length Video Generation From Open Domain Textual Descriptioncited by 484
- 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agentscited by 398
- 2016Unsupervised Learning for Physical Interaction through Video Predictioncited by 260
- 2021Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasetscited by 250
- 2022Pre-Trained Language Models for Interactive Decision-Makingcited by 221
- 2023Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMCcited by 220
- 2022Interactive Language: Talking to Robots in Real Timecited by 218
- 2015Action-Conditional Video Prediction using Deep Networks in Atari Gamescited by 172
- 2023Foundation Models for Decision Making: Problems, Methods, and Opportunitiescited by 164
- 2020A Review on Deep Learning Techniques for Video Predictioncited by 114
- 2023Compositional 3D Scene Generation using Locally Conditioned Diffusioncited by 101
- 2023Compositional Foundation Models for Hierarchical Planningcited by 95
- 2021FitVid: Overfitting in Pixel-Level Video Predictioncited by 87
- 2021Learning to Compose Visual Relationscited by 72
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- 2021Clockwork Variational Autoencoderscited by 42
- 2023Compositional Diffusion-Based Continuous Constraint Solverscited by 40
- 2019Model Based Planning with Energy Based Modelscited by 33
- 2023Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangementcited by 30
- 2023Probabilistic Adaptation of Text-to-Video Modelscited by 29
- 2021Composable Energy Policies for Reactive Motion Generation and Reinforcement Learningcited by 16
- 2022ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Timecited by 16
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Citing papers per year: 2023: 3; 2024: 54; 2025: 57; 2026: 48.
162 papers.
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