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- W3133095384 abstract "Robots encounter many risks that threaten the success of practical locomotion tasks. Legs break, electrical components overheat, and feet can unexpectedly slip. When all risks cannot be completely avoided, how does a robot decide its best action? We present a method for planning robot motions by reasoning about risk-of-failure probabilities instead of applying cost-penalty functions or inflexible path constraints. This work develops a risk-constrained formulation that can be straightforwardly included in existing motion planning optimizations. The risk constraints scale tractably with many risk sources, and in some cases, only add linear constraints to the optimization problem and are therefore compatible with model-predictive control techniques. We present a toy Puck World proof-of-concept example and a practical implementation on a planar monopod robot that runs at 3.2 m/s when permitted to take high-risk maneuvers. We believe this risk approach can be used to optimize robot behaviors under numerous conflicting task pressures and model risk-conscious behaviors in animals." @default.
- W3133095384 created "2021-03-01" @default.
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- W3133095384 date "2020-10-24" @default.
- W3133095384 modified "2023-10-16" @default.
- W3133095384 title "Risk-constrained Motion Planning for Robot Locomotion: Formulation and Running Robot Demonstration" @default.
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- W3133095384 doi "https://doi.org/10.1109/iros45743.2020.9340810" @default.
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