Dynamics-Aligned Shared Hypernetworks for Zero-Shot Actuator Inversion

• URL: http://arxiv.org/abs/2602.06550v1
• Date: Fri, 06 Feb 2026 09:55:05 GMT
• Title: Dynamics-Aligned Shared Hypernetworks for Zero-Shot Actuator Inversion
• Authors: Jan Benad, Pradeep Kr. Banerjee, Frank Röder, Nihat Ay, Martin V. Butz, Manfred Eppe,
• Abstract summary: We propose DMA*-SH, a framework where a single hypernetwork, trained solely via dynamics prediction, generates a small set of adapter weights.<n>This shared modulation imparts an inductive bias matched to actuator inversion, while input/output normalization and random input masking stabilize context inference.<n>For evaluation, we introduce the Actuator Inversion Benchmark (AIB), a suite of environments designed to isolate discontinuous context-to-dynamics interactions.
• Score: 3.335249027791264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot generalization in contextual reinforcement learning remains a core challenge, particularly when the context is latent and must be inferred from data. A canonical failure mode is actuator inversion, where identical actions produce opposite physical effects under a latent binary context. We propose DMA*-SH, a framework where a single hypernetwork, trained solely via dynamics prediction, generates a small set of adapter weights shared across the dynamics model, policy, and action-value function. This shared modulation imparts an inductive bias matched to actuator inversion, while input/output normalization and random input masking stabilize context inference, promoting directionally concentrated representations. We provide theoretical support via an expressivity separation result for hypernetwork modulation, and a variance decomposition with policy-gradient variance bounds that formalize how within-mode compression improves learning under actuator inversion. For evaluation, we introduce the Actuator Inversion Benchmark (AIB), a suite of environments designed to isolate discontinuous context-to-dynamics interactions. On AIB's held-out actuator-inversion tasks, DMA*-SH achieves zero-shot generalization, outperforming domain randomization by 111.8% and surpassing a standard context-aware baseline by 16.1%.

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