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The paper discusses limitations of stochastic single shooting methods like MPPI in robotics, particularly their sample inefficiency in satisfying terminal constraints over long action sequences. It proposes stochastic multiple shooting trajectory optimization via sequential local policy evaluation...
The paper discusses limitations of stochastic single shooting methods like MPPI in robotics, particularly their sample inefficiency in satisfying terminal constraints over long action sequences. It proposes stochastic multiple shooting trajectory optimization via sequential local policy evaluation...
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Stochastic single shooting trajectory optimization methods such as Model Predictive Path Integral control (MPPI) have been widely adopted in robotics due to their ability to reason about probabilistic dynamics and provide solutions where model gradients are noisy, costly to evaluate, or unavailable. However,...
This method could enhance trajectory optimization by better handling probabilistic dynamics and noisy or unavailable model gradients, potentially improving robotic control in complex environments. Addressing sample inefficiency may lead to more reliable and efficient robotic motion planning.
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VQV surfaced this signal because it is recent, relevant to Robotics, connected to arXiv.
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