← Back to all news
OpenAI Blog March 21, 2017

One-shot imitation learning

Read the original on OpenAI Blog →

The Flow has not summarised this story yet — read it at OpenAI Blog.

Related stories

OpenAI Blog
Mar 6, 2017

Third-person imitation learning

More like this →
arXiv Machine Learning
Jun 25

Consistent Zero-Shot Imitation with Contrastive Goal Inference

arXiv:2510. 17059v2 Announce Type: replace Abstract: Zero-shot imitation learning requires an agent to reproduce expert behavior from a single demonstration without additional environment interaction or gradient updates at test time.

By Kathryn Wantlin, Chongyi Zheng, Benjamin Eysenbach
agentsreinforcement-learningbenchmarks
More like this →
arXiv Machine Learning
Jun 16

DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy

arXiv:2506. 20668v3 Announce Type: replace-cross Abstract: We propose DemoDiffusion, a simple method for enabling robots to perform manipulation tasks by imitating a single human demonstration, without requiring task-specific training or paired human-robot data.

By Sungjae Park, Homanga Bharadhwaj, Shubham Tulsiani
diffusionreinforcement-learningrobotics
More like this →
arXiv AI
Jun 2

Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry

arXiv:2606. 01098v1 Announce Type: cross Abstract: Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control.

By Zemin Yang, Yaoyu He, Yiming Zhong, Yuhao Zhang, Xinge Zhu, Yao Mu, Qingqiu Huang, Yuexin Ma
diffusionroboticsefficiency
More like this →
arXiv AI
Jun 9

Difference-Aware Retrieval Policies for Imitation Learning

arXiv:2606. 09758v1 Announce Type: cross Abstract: Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding errors during deployment.

By Quinn Pfeifer, Ethan Pronovost, Paarth Shah, Khimya Khetarpal, Siddhartha Srinivasa, Abhishek Gupta
robotics
More like this →
arXiv Machine Learning
Jul 3

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning

arXiv:2602. 02762v2 Announce Type: replace Abstract: Semi-supervised imitation learning (SSIL) consists in learning a policy from a small dataset of action-labeled trajectories and a much larger dataset of action-free trajectories.

By Sacha Morin, Moonsub Byeon, Alexia Jolicoeur-Martineau, S\'ebastien Lachapelle
benchmarks
More like this →