2022
Learning Causal Relationships of Object Properties and Affordances Through Human Demonstrations and Self-Supervised Intervention for Purposeful Action in Transfer Environments
RA-L 2022
Learning object affordances enables robots to plan and perform purposeful actions. However, a fundamental challenge for the utilization of affordance knowledge lies in its generalization to unknown objects and environments. In this letter we present a new method for learning causal relationships bet