Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics

2026-06-10 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

Ambient Diffusion Policy is a novel method for imitation learning in robotics that effectively utilizes suboptimal data, addressing the challenge of expensive high-quality robot data versus abundant lower-quality demonstrations. Unlike existing co-training approaches that struggle to differentiate useful from harmful features in suboptimal samples, this policy introduces noise-dependent data usage, restricting suboptimal data's contribution to only high and low diffusion times during training. This approach is theoretically grounded in the observation that robot action data exhibits a spectral power law, leading to a global-to-local hierarchy and locality in the optimal Diffusion Policy. Validated across six tasks and four types of suboptimal action data (noisy trajectories, sim-to-real gap, task mismatch, large-scale data mixtures), Ambient Diffusion Policy significantly outperforms co-training baselines, achieving up to 33% better performance when scaled to the Open X-Embodiment dataset. This method enhances the utility of diverse data sources for robotic learning.

Key takeaway

For Machine Learning Engineers developing robot policies with limited high-quality data, you should consider Ambient Diffusion Policy to effectively integrate abundant suboptimal datasets. This method allows you to leverage noisy, sim-to-real, or mismatched data by restricting its influence to specific diffusion times, improving learning efficiency and performance. You can achieve up to 33% better results on heterogeneous datasets like Open X-Embodiment, expanding your usable data sources and reducing reliance on costly expert demonstrations.

Key insights

Ambient Diffusion Policy extracts useful features from suboptimal robot data by restricting its influence to specific diffusion times.

Principles

• Robot action data follows a spectral power law.
• This induces global-to-local hierarchy and locality.
• Noise-dependent data usage improves co-training.

Method

Ambient Diffusion Policy restricts suboptimal data contribution to high and low diffusion times during training. This leverages spectral power law properties to extract useful features, avoiding harmful ones.

In practice

• Use diverse, lower-quality datasets for robot training.
• Apply noise-dependent data usage in diffusion models.
• Improve performance on heterogeneous large-scale datasets.

Topics

• Imitation Learning
• Robotics
• Diffusion Models
• Suboptimal Data
• Open X-Embodiment
• Policy Learning

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

Related on AIssential

• X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations — X-Diffusion selectively integrates noisy human demonstrations into robot policy training to overcome embodiment mismatches.
• Decentralized Diffusion Policy Learning for Enhanced Exploration in Cooperative Multi-agent Reinforcement Learning — Diffusion policies enable multi-modal exploration in MARL, outperforming restrictive Gaussian policies.
• Difference-Aware Retrieval Policies for Imitation Learning — DARP enhances imitation learning generalization by leveraging local neighborhood structure and retrieval from expert demonstrations.
• DiffusionOPD: A Unified Perspective of On-Policy Distillation in Diffusion Models — DiffusionOPD unifies multi-task diffusion model training via online policy distillation, improving efficiency and performance.
• Generative adversarial imitation learning for robot swarms: Learning from human demonstrations and trained policies — A GAIL framework enables robot swarms to learn collective behaviors from human demonstrations, performing comparably to source policies.
• Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry — IDP enables one-step robot action generation by implicitly incorporating expert trajectory correction via conditional geometry, avoiding explicit drifting field estimation.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.