Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation

Title:Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation

Abstract:Multi-objective reinforcement learning for humanoid robots must coordinate locomotion and manipulation within a single policy. A natural design choice is whether to use a single (unified) critic that estimates the combined value of all objectives, or separate (dual) critics with disjoint reward signals. We present a controlled comparison on the Unitree G1 humanoid (23 active DoF) in NVIDIA Isaac Lab, training loco-manipulation policies through a sequential curriculum spanning 13 levels from stationary reaching to walking with variable-orientation targets. In standardized evaluation, dual-critic policies reach targets 3.5$\times$ faster (6.5 vs. 22.6 simulation steps), achieve 2$\times$ higher throughput (14.3 vs. 7.0 validated reaches per 1,000 steps), and attain higher validated reach rates (65.2% vs. 53.8%) compared to the unified-critic policy. Notably, additional anti-gaming reward mechanisms provide no further improvement beyond the architectural change alone (60.9% vs. 65.2%). These results have direct implications for the emerging paradigm of RL fine-tuning of imitation-learned policies: when refining a pre-trained manipulation policy with RL, a unified critic risks suppressing the learned behavior through competing locomotion gradients. These findings demonstrate that critic architecture is a primary - and often overlooked - design choice in multi-objective humanoid RL, with greater impact than reward engineering on reaching efficiency.

Accepted at the ICRA 2026 Workshop on Reinforcement Learning for Imitation Learning (RL4IL), Vienna, Austria. 4 pages, 2 figures

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2606.11891 [cs.RO]

(or arXiv:2606.11891v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.11891 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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