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

Summary

Abstract Summary

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.

Key Points

Controlled comparison of dual vs. unified critic architectures for humanoid loco-manipulation.
Dual-critic policies reach targets 3.5x faster and achieve 2x higher throughput than unified critics.
Critic architecture has greater impact on performance than reward engineering or anti-gaming mechanisms.
Findings are directly relevant to RL fine-tuning of pre-trained manipulation policies.

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