$\nabla$Mimic: Ultra-Efficient Humanoid Control with Differentiable Simulation
Overview
This paper introduces $\nabla$Mimic and NablaEngine, a differentiable GPU simulation engine for the Unitree G1. The authors address the limitations of zeroth-order methods like PPO and unstable first-order approaches by using exact simulator gradients bounded by gradient clipping and multistep regression. NablaEngine computes MuJoCo's full contact dynamics and adjoints as fused megakernels, achieving analytic gradients that match float64 finite differences and are 11.5 times faster than the available MuJoCo Warp adjoint. On whole-body tracking of all 40 LAFAN1 clips with one policy on one GPU, $\nabla$Mimic reaches a score of 0.82 in 20 minutes (matching the state-of-the-art world-model method) and 0.874 in 1.8 hours, whereas the standard first-order baseline SHAC stops due to exploding gradients within minutes. The code is released as open-source infrastructure.
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Environment: simulation
“arXiv:2610.41654v1 Announce Type: new Abstract: Robot policy learning is dominated by zeroth-order methods such as PPO, which treat the simulator as a black box and pay for it with enormous sample counts. First-order policy learning differentiates through the dynamics, but on contact-rich whole-body control it has rarely worked: its gradients explode. A recent world-model method showed that derivative information can”
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Reported quantity · Reported duration: 20 other · Basis not reported
“ll contact dynamics with fused per-world kernels and the adjoint of a whole control step as a single megakernel, with analytic gradients that match float64 finite differences, $11.5\times$ faster than the available MuJoCo Warp adjoint. On whole-body tracking of all 40 LAFAN1 clips with one policy on one GPU, $\nabla$Mimic reaches the score reported by the state-of-the-art world-model method, 0.82, in 20 minutes inste”
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Reported quantity · speedup_factor: 11.5 other · Basis not reported
“ll contact dynamics with fused per-world kernels and the adjoint of a whole control step as a single megakernel, with analytic gradients that match float64 finite differences, $11.5\times$ faster than the available MuJoCo Warp adjoint. On whole-body tracking of all 40 LAFAN1 clips with one policy on one GPU, $\nabla$Mimic reaches the score reported by the state-of-the-art world-model method, 0.82, in 20 minutes inste”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignal$\nabla$Mimic: Ultra-Efficient Humanoid Control with Differentiable Simulation
This paper introduces $\nabla$Mimic and NablaEngine, a differentiable GPU simulation engine for the Unitree G1. The authors address the limitations of zeroth-order methods like PPO and unstable first-order approaches by using exact simulator gradients bounded by gradient clipping and multistep regression. NablaEngine computes MuJoCo's full contact dynamics and adjoints as fused megakernels, achieving analytic gradients that match float64 finite differences and are 11.5 times faster than the available MuJoCo Warp adjoint. On whole-body tracking of all 40 LAFAN1 clips with one policy on one GPU, $\nabla$Mimic reaches a score of 0.82 in 20 minutes (matching the state-of-the-art world-model method) and 0.874 in 1.8 hours, whereas the standard first-order baseline SHAC stops due to exploding gradients within minutes. The code is released as open-source infrastructure.
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