From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation
From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation explores A framework for real-time multi-modal trajectory policies in robotic manipulation using single-step acceleration methods.. Commercial viability score: 6/10 in Robotic Manipulation.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisFlow Policy Gradients for Robot ControlViability: 5.0
- Higher ViabilityOne-Step Flow Policy: Self-Distillation for Fast Visuomotor PoliciesViability: 8.0
- Higher ViabilityAda3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic ManipulationViability: 7.0
- Higher ViabilityProbeFlow: Training-Free Adaptive Flow Matching for Vision-Language-Action ModelsViability: 8.0
- Higher ViabilityGenerative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control