DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning
This addresses a key problem in robotics and AI by improving task performance in model-based learning, though it appears incremental as it builds on existing differentiable optimization techniques.
The paper tackles the objective mismatch issue in model-based reinforcement and imitation learning by introducing DiffTORI, which uses differentiable trajectory optimization to learn cost and dynamics functions end-to-end, resulting in outperforming prior state-of-the-art methods across 15 RL and 35 imitation learning tasks with high-dimensional inputs.
This paper introduces DiffTORI, which utilizes Differentiable Trajectory Optimization as the policy representation to generate actions for deep Reinforcement and Imitation learning. Trajectory optimization is a powerful and widely used algorithm in control, parameterized by a cost and a dynamics function. The key to our approach is to leverage the recent progress in differentiable trajectory optimization, which enables computing the gradients of the loss with respect to the parameters of trajectory optimization. As a result, the cost and dynamics functions of trajectory optimization can be learned end-to-end. DiffTORI addresses the ``objective mismatch'' issue of prior model-based RL algorithms, as the dynamics model in DiffTORI is learned to directly maximize task performance by differentiating the policy gradient loss through the trajectory optimization process. We further benchmark DiffTORI for imitation learning on standard robotic manipulation task suites with high-dimensional sensory observations and compare our method to feed-forward policy classes as well as Energy-Based Models (EBM) and Diffusion. Across 15 model-based RL tasks and 35 imitation learning tasks with high-dimensional image and point cloud inputs, DiffTORI outperforms prior state-of-the-art methods in both domains. Our code is available at https://github.com/wkwan7/DiffTORI.