arXiv AI July 2, 2026 By Chih-Han Yang, Dai-Jie Wu, Yun-Ping Huang, Ping-Chun Hsieh, Kenneth Marino, Shao-Hua Sun

Language-Critique Imitation Learning from Suboptimal Demonstrations

arXiv:2607. 01225v1 Announce Type: cross Abstract: Prior work on imitation learning from suboptimal demonstrations typically relies on compressed supervision signals such as confidence estimates, discriminator scores, or importance weights.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 7

Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning

arXiv:2508. 08983v2 Announce Type: replace-cross Abstract: Humans can learn a new manipulation task from one or two demonstrations and then perform it in a new room, with new objects, under new constraints.

By Ben Zandonati, Tom\'as Lozano-P\'erez, Leslie Pack Kaelbling

llms robotics multimodal benchmarks

arXiv Machine Learning

Jun 2

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.

By Christian Scherer, Joe Watson, Theo Gruner, Daniel Palenicek, Ingmar Posner, Jan Peters

reinforcement-learning robotics fine-tuning

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

arXiv AI

Jul 3

Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs

arXiv:2607. 02466v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are fundamentally bottlenecked by the scarcity of expert demonstrations -- triplets of observations, instructions, and actions that are costly to collect at scale.

By Junhao Shi, Siyin Wang, Xiaopeng Yu, Li Ji, Jingjing Gong, Xipeng Qiu

agents robotics multimodal benchmarks safety

arXiv Machine Learning

Jun 2

Training-Free Imitation Learning with Closed-Form Diffusion Policies

arXiv:2606. 01238v1 Announce Type: cross Abstract: While diffusion-based policies have impressive performance and expressivity, their long offline training slows down the data collection and policy deployment loop.

By Raghav Mishra, Ian R. Manchester

diffusion benchmarks

arXiv Machine Learning

Jul 8

Supervised Reward Inference

arXiv:2502. 18447v2 Announce Type: replace Abstract: Existing approaches to reward inference typically assume that humans provide demonstrations according to specific behavior models.

By Will Schwarzer, Jordan Schneider, Philip S. Thomas, Scott Niekum

robotics benchmarks

One email a morning, machine-written

Related stories

Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

Difference-Aware Retrieval Policies for Imitation Learning

Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs

Training-Free Imitation Learning with Closed-Form Diffusion Policies

Supervised Reward Inference