Knowledge- and ambiguity-aware robot learning from corrective and evaluative feedback

In order to deploy robots that could be adapted by non-expert users, interactive imitation learning (IIL) methods must be flexible regarding the interaction preferences of the teacher and avoid assumptions of perfect teachers (oracles), while considering they make mistakes influenced by diverse human factors. In this work, we propose an IIL...

Uncertainties based queries for Interactive policy learning with evaluations and corrections

Interactive Learning with Corrective Feedback for Policies Based on Deep Neural Networks

Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs). However, it is highly data demanding, so unfeasible in physical systems for most applications. In this work, we approach an alternative Interactive Machine Learning (IML) strategy for training DNN...

Learning Interactively to Resolve Ambiguity in Reference Frame Selection

In Learning from Demonstrations, ambiguities can lead to bad generalization of the learned policy. This paper proposes a framework called Learning Interactively to Resolve Ambiguity (LIRA), that recognizes ambiguous situations, in which more than one action have similar probabilities, avoids a random action selection, and uses the human...

Interactive Imitation Learning in State-Space

Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available demonstration data. Interactive Imitation Learning techniques can improve the efficacy of learning since they involve teachers providing feedback while the agent...

Reinforcement learning of motor skills using Policy Search and human corrective advice

Robot learning problems are limited by physical constraints, which make learning successful policies for complex motor skills on real systems unfeasible. Some reinforcement learning methods, like Policy Search, offer stable convergence toward locally optimal solutions, whereas interactive machine learning or learning-from-demonstration methods...

A fast hybrid reinforcement learning framework with human corrective feedback

Reinforcement Learning agents can be supported by feedback from human teachers in the learning loop that guides the learning process. In this work we propose two hybrid strategies of Policy Search Reinforcement Learning and Interactive Machine Learning that benefit from both sources of information, the cost function and the human corrective...