Why Koopman Structure Does Not Help Differentiable World Models for Humanoid Whole-Body Tracking

arXiv:2611.00951v1 Announce Type: new Abstract: We test whether Koopman structure, linear dynamics in a lifted state space, helps when a humanoid whole-body tracking policy is trained by backpropagating through a differentiable world model, replacing the MLP world model of an otherwise unchanged pipeline for simulated Unitree G1 tracking. It does not: return is at best equal (0.823 versus 0.830), one-step error on identical data is 1.3 to 1.6 times the MLP's, and training is about 15% slower. Lifted rollouts diverge or fail to train a policy; of the schemes tested, re-lifting at every imagined step trains best. Re-lifting never uses the lifted linear dynamics and leaves a model affine in the action, with state-dependent offset and slope; an action-affine MLP also predicts worse, so the cost belongs to this class. At a fixed state, the curvature of the true dynamics in the action explains only part of it. Across states, the policy's actions span a curved region, so an affine model fits policy data only as the tangent at the policy's action, the operating point: on G1, the MLP's tangent there is as accurate as the MLP; at the mean action, 31 times worse. The offset must encode, from the state alone, where the policy acts, whereas an MLP reads the action. Supplying the policy's action removes most of the remaining gap on G1, Ant and Humanoid. The only online repair approaching the MLP feeds the action into the nonlinear lift, giving up the structure. The MLP world model thus remains the better choice here.

Abstract:We test whether Koopman structure, linear dynamics in a lifted state space, helps when a humanoid whole-body tracking policy is trained by backpropagating through a differentiable world model, replacing the MLP world model of an otherwise unchanged pipeline for simulated Unitree G1 tracking. It does not: return is at best equal (0.823 versus 0.830), one-step error on identical data is 1.3 to 1.6 times the MLP's, and training is about 15% slower. Lifted rollouts diverge or fail to train a policy; of the schemes tested, re-lifting at every imagined step trains best. Re-lifting never uses the lifted linear dynamics and leaves a model affine in the action, with state-dependent offset and slope; an action-affine MLP also predicts worse, so the cost belongs to this class. At a fixed state, the curvature of the true dynamics in the action explains only part of it. Across states, the policy's actions span a curved region, so an affine model fits policy data only as the tangent at the policy's action, the operating point: on G1, the MLP's tangent there is as accurate as the MLP; at the mean action, 31 times worse. The offset must encode, from the state alone, where the policy acts, whereas an MLP reads the action. Supplying the policy's action removes most of the remaining gap on G1, Ant and Humanoid. The only online repair approaching the MLP feeds the action into the nonlinear lift, giving up the structure. The MLP world model thus remains the better choice here.

Robotics (cs.RO)

arXiv:2611.00951 [cs.RO]

(or arXiv:2611.00951v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2611.00951 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Why Koopman Structure Does Not Help Differentiable World Models for Humanoid Whole-Body Tracking

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