LfC Workshop @ CoRL 2026

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. 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.