TieBot: Learning to Knot a Tie from Visual Demonstration through a Real-to-Sim-to-Real Approach
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The tie-knotting task is highly challenging due to the tie's high deformation and long-horizon manipulation actions. This work presents TieBot, a Real-to-Sim-to-Real learning from visual demonstration system for the robots to learn to knot a tie. We introduce the Hierarchical Feature Matching approach to estimate a sequence of tie's meshes from the demonstration video. With these estimated meshes used as subgoals, we first learn a teacher policy using privileged information. Then, we learn a student policy with point cloud observation by imitating teacher policy. Lastly, our pipeline applies learned policy to real-world execution. We demonstrate the effectiveness of TieBot in simulation and the real world. In the real-world experiment, a dual-arm robot successfully knots a tie, achieving 50% success rate among 10 trials. Videos can be found https://tiebots.github.io/.
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Cited by 3 Pith papers
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DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning
DeformGen augments states via localized disturbance simulation and trajectories via deformation-field warping to improve deformable manipulation policy learning over original data and rigid baselines.
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RoboHitch: Learning Visual Affordance from Disordered Keypoints for Hitch Knots Tying
A learning framework that predicts pick-and-place affordances for hitch knots from unordered keypoints and images via graph and convolutional autoencoders fused by cross-attention.
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