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We first demonstrate the efficacy of our method on a set of new deformable object manipulation tasks that are previously unsolvable by the state-of-the-art methods. Our experiments show that our method outperforms the state-of-the-art reinforcement learning methods and the differentiable simulator-based trajectory optimizer for deformable object manipulation tasks.
DiffSkill has two main benefits over existing methods. First, it learns skills from demonstration trajectories. Therefore, the learned skills are not task-specific, and we can easily apply the same skills to different tasks. Second, it is based on differentiable physics simulators, which allow us to learn skills with much higher fidelity compared to the differentiable simulator-based trajectory optimizer. We also demonstrate how our framework can support multi-agent systems or robots with multiple tools and sensor setups, and we plan for long-horizon tasks.
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We present a new cost function-based framework for finding graph representations of events that are optimal for temporal reasoning. We show that this framework can be used to predict the unfolding of occlusions, which are a major challenge for real-time 3D visual odometry. We use this framework to tackle occlusions by automatically choosing an optimal graph for a given 3D input, which we then use as the input for our visual odometry algorithm. 827ec27edc