Paper ID: 2401.17500 • Published Jan 30, 2024
LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory Optimization
Zhengtong Xu, Yu She
TL;DR
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This paper introduces LeTO, a method for learning constrained visuomotor
policy with differentiable trajectory optimization. Our approach integrates a
differentiable optimization layer into the neural network. By formulating the
optimization layer as a trajectory optimization problem, we enable the model to
end-to-end generate actions in a safe and constraint-controlled fashion without
extra modules. Our method allows for the introduction of constraint information
during the training process, thereby balancing the training objectives of
satisfying constraints, smoothing the trajectories, and minimizing errors with
demonstrations. This ``gray box" method marries optimization-based safety and
interpretability with powerful representational abilities of neural networks.
We quantitatively evaluate LeTO in simulation and in the real robot. The
results demonstrate that LeTO performs well in both simulated and real-world
tasks. In addition, it is capable of generating trajectories that are less
uncertain, higher quality, and smoother compared to existing imitation learning
methods. Therefore, it is shown that LeTO provides a practical example of how
to achieve the integration of neural networks with trajectory optimization. We
release our code at this https URL