LeapBot-JAC: Joint Action Chains for World Action Models
cs.RO
Submitted: 2026-10-09
Updated: 2026-10-09
Code: https://github.com/LeapWM/leapbot-jac
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- Motus: A Unified Latent Action World Model
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
- Learning Universal Policies via Text-Guided Video Generation
- Causal World Modeling for Robot Control
- Flow Matching for Generative Modeling
- LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
- LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments
- Learning Latent Plans from Play
- RoFormer: Enhanced Transformer with Rotary Position Embedding
- Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models
- MemoryWAM: Efficient World Action Modeling with Persistent Memory
- MaskWAM: Unifying Mask Prompting and Prediction for World-Action Models
- Fast-WAM: Do World Action Models Need Test-time Future Imagination?
- Robotic Control via Embodied Chain-of-Thought Reasoning
- ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies