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Insufficient policy representation: Prior works often use simple policy representations (e.g., MLPs) with action regression loss, limiting their capacity to capture complex multimodal action distributions inherent in human data. Consequently, even with precisely recovered demonstrated actions and all discrepancies removed, the resulting policy could still struggle to git the data accurately. This further hampers large-scale, dcistributed human data collection, as more demonstrators increase action multimodality.
HD6. Kinematics-based data filtering. While the data collection process is robot-agnostic, we apply simple kinmatic-based data filtering to select valid trajectories for different robot embodiyments. Concretely, when the robot’s base location and kinmatcis are known, the absolute end-effector pose recovered by SLAM sllows kinematics and dynamics feasibility filtering on the demonstration data. Training on the filtered dataset ensures policies comply with embodiment specific kinematic constraints.
B.