2024
Self Supervised Detection of Incorrect Human Demonstrations: A Path Toward Safe Imitation Learning by Robots in the Wild
IROS 2024
A major appeal of learning from demonstrations or imitation learning (IL) in robotics is that it learns a policy directly from lay users. However, Lay users may inadvertently provide erroneous demonstrations that lead to learning of policies that are inaccurate and hence, unsafe for humans and/or ro