Dynamic Manipulation with World-Action Models via Counterfactual Planning

arXiv:2609.33172 · cs.RO, cs.AI, cs.CV, cs.LG · Submitted 2026-09-27 · Read on arXiv

cs.RO, cs.AI, cs.CV, cs.LG

Submitted: 2026-09-27

Updated: 2026-09-27

Project page: https://methoder00.github.io/DPP

Terminology

• Dynamic Predictive Planning — 16× in this paper
• Ablation — 14× in this paper · explained in 1 episode
• Alignment — 12× in this paper · explained in 1 episode
• Adaptation — 11× in this paper · explained in 1 episode
• Action Chunking — 8× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• World Model — 6× in this paper · explained in 7 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Core — 5× in this paper · explained in 2 episodes
• Route Completion — 4× in this paper
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Steering — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Action Decoder — 2× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• In International Conference on Machine Learning — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Digital Twin — 1× in this paper · explained in 4 episodes
• Frozen model — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• International Conference on Automation Science and Engineering — 1× in this paper
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Flash-WAM: Modality-Aware Distillation for World Action Models
• Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models
• AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
• GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
• Self-Supervised Visual Planning with Temporal Skip Connections
• Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
• TEMPO: Learning Temporal Context for Dynamic Robot Manipulation
• RLDX-1 Technical Report
• DART: Noise Injection for Robust Imitation Learning
• Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination
• Adaptive Action Chunking at Inference-time for Vision-Language-Action Models
• Delay-Aware Diffusion Policy: Bridging the Observation-Execution Gap in Dynamic Tasks
• DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration
• VLS: Steering Pretrained Robot Policies via Vision-Language Models
• DynamicWAM: Dual-Path Motion Conditioning for World-Action Models in Dynamic Manipulation
• RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins
• Cosmos 3: Omnimodal World Models for Physical AI
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon
• SAM 2: Segment Anything in Images and Videos

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