Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a novel method for robust learning from demonstration using leveraged Gaussian process regression. While existing learning from demonstration (LfD) algorithms assume that demonstrations are given from skillful experts, the proposed method alleviates such assumption by allowing demonstrations from casual or novice users. To learn from demonstrations of mixed quality,  present a sparse-constrained leveraged optimization algorithm using proximal linearized minimization. The proposed sparse constrained leverage optimization algorithm is successfully applied to sensory field reconstruction and direct policy learning for planar navigation problems. In experiments, the proposed sparse-constrained method outperforms existing LfD methods.
['Sungjoon Choi', 'Kyungjae Lee', 'Songhwai Oh']
I am writing a research paper on a idea Robust learning from demonstration using leveraged Gaussian processes and sparse-constrained optimization
[{"from": "human", "value": "I am writing a research paper on a idea Robust learning from demonstration using leveraged Gaussian processes and sparse-constrained optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a novel method for robust learning from demonstration using leveraged Gaussian process regression. While existing learning from demonstration (LfD) algorithms assume that demonstrations are given from skillful experts, the proposed method alleviates such assumption by allowing demonstrations from casual or novice users. To learn from demonstrations of mixed quality,  present a sparse-constrained leveraged optimization algorithm using proximal linearized minimization. The proposed sparse constrained leverage optimization algorithm is successfully applied to sensory field reconstruction and direct policy learning for planar navigation problems. In experiments, the proposed sparse-constrained method outperforms existing LfD methods."}]