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TRACE: Learned Proprioceptive Odometry for Legged Robots under Unreliable Contact Conditions
An end-to-end learned proprioceptive odometry estimator for legged robots under unreliable contact conditions that directly predicts relative displacement, relative rotation, and body-frame velocity from a recent history of onboard inertial and joint measurements.
PAKE: Learning Whole-Body Loco-Manipulation with Partial Kinematic Embeddings
This paper introduces a new Kinematic Normalizing Flow (KNF) model, trained on a large-scale kinematic dataset, that generates diverse yet feasible partial reference motions that effectively addresses whole-body control challenges by decomposing the complex loco-manipulation problem into partial reference motion genera...