Cue the Flow: Steering Flow-Matching Policies for Open-World Delivery Manipulation

arXiv:2609.38989 · cs.RO · Submitted 2026-09-30 · Read on arXiv

cs.RO

Submitted: 2026-09-30

Updated: 2026-09-30

Project page: https://hatchetproject.github.io/delivery_steer

Terminology

• Steering — 33× in this paper · explained in 2 episodes
• Grounding — 29× in this paper · explained in 3 episodes
• Generalization — 13× in this paper · explained in 13 episodes
• Affordance — 6× in this paper · explained in 1 episode
• Robustness — 6× in this paper · explained in 5 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Distribution Shift — 3× in this paper · explained in 6 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• OpenVLA: An Open-Source Vision-Language-Action Model
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy
• THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation
• MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting
• RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics
• Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
• kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation
• TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
• VCA: Vision-Click-Action Framework for Precise Manipulation of Segmented Objects in Target Ambiguous Environments
• SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
• Spatial-Aware VLA Pretraining through Visual-Physical Alignment from Human Videos
• Much Ado About Noising: Dispelling the Myths of Generative Robotic Control
• Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models
• PointVLA: Injecting the 3D World into Vision-Language-Action Models
• Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance
• OmniGuide: Universal Guidance Fields for Enhancing Generalist Robot Policies

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