cuTAMP combines GPU-parallelized sampling and differentiable optimization to solve long-horizon task and motion planning problems on constrained manipulation tasks, outperforming serial baselines.
A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization
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abstract
Many problems in modern robotics can be addressed by modeling them as bilevel optimization problems. In this work, we leverage augmented Lagrangian methods and recent advances in automatic differentiation to develop a general-purpose nonlinear optimization solver that is well suited to bilevel optimization. We then demonstrate the validity and scalability of our algorithm with two representative robotic problems, namely robust control and parameter estimation for a system involving contact. We stress the general nature of the algorithm and its potential relevance to many other problems in robotics.
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cs.RO 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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Differentiable GPU-Parallelized Task and Motion Planning
cuTAMP combines GPU-parallelized sampling and differentiable optimization to solve long-horizon task and motion planning problems on constrained manipulation tasks, outperforming serial baselines.