PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking
cs.RO
Submitted: 2026-05-30
Updated: 2026-09-27
Comments: 21 pages, 7 figures, 6 tables. Preprint
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Mixture of Horizons in Action Chunking
- OpenVLA: An Open-Source Vision-Language-Action Model
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- Real-Time Robot Execution with Masked Action Chunking
- VLA Knows Its Limits: Adaptive Execution Horizons for Robot Policies
- World Action Models are Zero-shot Policies
- 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations
- JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy
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