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• W2564446177 abstract "We present SaDDP, a sampled version of the widely used differential dynamic programming (DDP) control algorithm. We contribute through establishing a novel connection between two major branches of robotics control research, that is, gradient-based methods such as DDP, and Monte Carlo methods such as path integral control (PI) that utilize random simulated trajectory rollouts. One of our key observations is that the Taylor-expansion, central to DDP, can be reformulated in terms of second-order statistics computed of the sampled trajectories. SaDDP makes few assumptions about the controlled system and works with black-box dynamics simulations with non-smooth contacts. Our simulation results show that the method outperforms PI and CMA-ES in both a simple linear-quadratic problem, and a multilink arm reaching task with obstacles." @default.
• W2564446177 created "2017-01-06" @default.
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• W2564446177 date "2016-10-01" @default.
• W2564446177 modified "2023-10-16" @default.
• W2564446177 title "Sampled differential dynamic programming" @default.
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• W2564446177 doi "https://doi.org/10.1109/iros.2016.7759229" @default.
• W2564446177 hasPublicationYear "2016" @default.
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