REVIEW 13 cited by
Learning Universal Policies via Text-Guided Video Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks. Recent progress in text-guided image synthesis has yielded models with an impressive ability to generate complex novel images, exhibiting combinatorial generalization across domains. Motivated by this success, we investigate whether such tools can be used to construct more general-purpose agents. Specifically, we cast the sequential decision making problem as a text-conditioned video generation problem, where, given a text-encoded specification of a desired goal, a planner synthesizes a set of future frames depicting its planned actions in the future, after which control actions are extracted from the generated video. By leveraging text as the underlying goal specification, we are able to naturally and combinatorially generalize to novel goals. The proposed policy-as-video formulation can further represent environments with different state and action spaces in a unified space of images, which, for example, enables learning and generalization across a variety of robot manipulation tasks. Finally, by leveraging pretrained language embeddings and widely available videos from the internet, the approach enables knowledge transfer through predicting highly realistic video plans for real robots.
Forward citations
Cited by 13 Pith papers
-
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
Embodied.cpp introduces a portable C++ inference runtime with modular layers for deploying VLA and WAM models on heterogeneous robots, reporting 100% and 91% task success on two models plus memory reduction on a WAM b...
-
In-Context World Modeling for Robotic Control
Prepending a few self-generated random interaction clips as context lets VLA policies identify novel camera viewpoints and morphologies at test time and outperform multi-view baselines without parameter updates.
-
Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills
A taxonomy of robot learning on a weights-versus-skills axis, with a five-rung self-improvement ladder whose top cell (feedback plus memory plus search) holds only a few recent systems.
-
HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone
Robot-free HiFi-UMI demonstrations can replace teleoperated real-robot data in post-training: three policy backbones matched in-domain teleoperation within 3.1 percentage points, including 85% success on a precision i...
-
RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation
Dense per-frame intermediate representations (traces, masks, grasp poses, subtasks) improve embodied VQA, VLA action generation, and world-model video prediction in the new 230k-episode RoboInter-Data suite.
-
DIM-WAM: World-Action Modeling with Diverse Historical Event Memory
Multi-bank similarity-merged event memory plus progress supervision raises long-horizon WAM success from 28.4% to 69.8% on RMBench and full-task success from 52.5% to 80% on real Franka tasks.
-
When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning
Adaptively gating and scaling world-model imagination at test time matches or outperforms always-on imagination on spatial reasoning benchmarks while using substantially fewer world-model calls and tokens.
-
AMPLIFY: Actionless Motion Priors for Robot Learning from Videos
A three-stage pipeline that turns keypoint tracks into discrete motion tokens, predicts them from action-free video, and decodes them into actions yields large few-shot and zero-shot policy improvements in robot manipulation.
-
Self-Consistent Model-based Adaptation for Visual Reinforcement Learning
SCMA trains a policy-agnostic observation denoiser, using a pre-trained world model as a clean-distribution reference, and shows improved visual RL performance under distractions.
-
Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models
Robust-WAM aligns action-stream query tokens with future-frame semantic embeddings during training, improving out-of-distribution robustness of video-generation-based robot policies without sacrificing in-distribution...
-
From World Models to World Action Models: A Concise Tutorial for Robotics
World models are action-conditioned predictors of task-relevant futures; world action models couple those futures to robot actions via four paradigms: imagine-then-execute, feature-conditioned, joint, and auxiliary pr...
-
Data Pyramid for Embodied Manipulation
Embodied training data form a five-layer pyramid—real-robot, UMI, ego/exo, simulation, general V–L—ordered by the trade-off between scale and robot alignment, and model capabilities track how those layers are mixed.
-
Reinforcement Learning: From Algorithms To Foundation Models
A dissertation uniting the author's published results: non-exploitable Nash-DQN policies and the FightLadder benchmark for games, plus diffusion/consistency-model world models for RL — a compilation rather than new results.
Discussion (0). Continue with ORCID to comment.