arXiv Machine Learning

Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach

arXiv:2605. 07166v2 Announce Type: replace Abstract: Imitation learning is widely used for learning to act in complex environments.

arXiv Machine Learning
Aug 3

Mirror Learning

arXiv:2607. 28737v1 Announce Type: new Abstract: We investigate imitation learning through the lens of third-person observation and propose a framework for mirror learning: acquiring actionable policies from passive observation.

By Yunpeng Liu, Matthew Niedoba, Oluwanifemi A. Adekanye, Jason Yoo, Yingchen He, Berend Zwartsenberg, Frank Wood
arXiv AI
Aug 6

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning

arXiv:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.

By Agnese Chiatti, Michael Cochez, Cristina Cornelio, Sebastijan Dumancic, Artur d'Avila Garcez, Luis C. Lamb, Lia Morra, Mathias Niepert, Robert Peharz, Alberto Speranzon, Maarten Stol, Annette Ten Teije, Thiviyan Thanapalasingam, Frank Van Harmelen, Emile Van Krieken, Antonio Vergari, Benjie Wang
arXiv Machine Learning
Aug 7

Robot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness Evaluation

arXiv:2608. 06221v1 Announce Type: cross Abstract: Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot.

By Alperen Kenan, Paul Bremner, Manuel Giuliani
Hugging Face Trending Papers
Jun 24

ROAD-VLA: Robust Online Adaptation via Self-Distillation for Vision-Language-Action Models

Effective online adaptation of vision-language-action (VLA) models remains challenging, as sparse rewards provide weak supervision for high-dimensional autoregressive action policies. Although self-distillation can in principle provide denser training signals, we find that text-based privileged teachers conditioned on demonstrations, retrieved experiences, or high-level plans are ineffective for VLA adaptation, exposing a modality gap between symbolic guidance and low-level robot actions.