Interactive learning of sensor policy fusion

Teaching a robot how to navigate in a new environment only from the sensor input in an end-to-end fashion is still an open challenge with much attention from industry and academia. This paper proposes an algorithm with the name 'Learning Interactively to Resolve Ambiguity' (LIRA) that tackles the problem of sensor policy fusion extending state- of-the-art methods by employing ambiguity awareness in the decision-making and solving it using active and interactive querying of the human expert. LIRA, in fact, employs Gaussian Processes for the estimation of the policy's confidence and investigates the ambiguity due to the disagreement between the single sensor policies on the desired action to take. LIRA aims to make the teaching of new policies easier, learning from human demonstrations and correction.The experiments show that LIRA can be used for learning a sensor-fused policy from scratch or also leveraging the knowledge of existing single sensor policies. The experiments focus on the estimation of the human interventions required for teaching a successful navigation policy.
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Learning & Autonomous Control