Workshop on Learning from Corrections and Interventions

Nov 12, 2026, Austin, Texas

Robots operating in real-world environments often learn from human demonstrations, preferences, or rewards. However, in many deployment settings, people act as supervisors rather than teachers: they monitor robot behavior and intervene only when necessary. These corrections and interventions provide rich information about task objectives, constraints, and human expectations, while requiring less effort than continuous supervision.

Learning from Corrections (LfC) has emerged as a promising paradigm for interactive robot learning, but many fundamental questions remain:

What constitutes a correction or intervention?
What information is contained in corrective behavior?
Why and when do people intervene?
How can robots learn from noisy and imperfect corrections?
How can correction-based learning scale to real-world robotic systems?

This workshop brings together researchers from robotics, machine learning, human-robot interaction, and related application domains to discuss the foundations, challenges, and future directions of Learning from Corrections.

Topics

1. Defining and Representing Correction Signals

Definitions, taxonomies, and terminology for corrections and interventions
Explicit versus implicit corrective feedback
Multimodal correction signals
Datasets and data collection methodologies
Standardized representations and annotations

2. Understanding Human Corrective Behavior

Trust, expectations, and perceived robot competence
Human supervision strategies
Mental models and human-robot alignment
Intervention timing and prediction
Individual differences and personalization

3. Learning from Noisy and Imperfect Corrections

Learning reward functions, policies, and constraints
Modeling uncertainty and noisy feedback
Learning from explicit and implicit correction signals
Adapting methods from demonstrations and preferences
Evaluation metrics and benchmarks

4. Leveraging Corrections in Real-World Systems

Shared autonomy and supervisory control
Assistive robotics
Foundation-model robotics
Human-AI oversight
Safety-critical systems
Industry deployment of intervention-based learning

Call for Papers

We invite submissions on all aspects of Learning from Corrections and Interventions, including:

Correction representations
Human factors
Interactive robot learning
Shared autonomy
Preference learning
Foundation-model robotics
Real-world deployment

We welcome submissions in two formats: extended abstracts (up to 2 pages) and full papers (up to 6 pages), excluding references. Submissions should use the official CoRL 2026 paper template and should not include an appendix or supplementary material. All submissions will undergo double-blind review, so authors should anonymize their submissions and avoid information that reveals their identities.

Both formats are encouraged for work relevant to Learning from Corrections and Interventions, including completed research, work in progress, position papers, datasets, and benchmark proposals. Accepted submissions from both formats will be featured in lightning talks and poster presentations during the workshop.

Submissions should be made through OpenReview: https://openreview.net/group?id=robot-learning.org/CoRL/2026/Workshop/LfC

Timeline: