Study Finds Dual-Critic Architecture Outperforms Unified Approach for Humanoid Robot Control
Researchers compared two reinforcement learning architectures for controlling humanoid robots performing combined locomotion and manipulation tasks, finding that dual critics (separate value estimators for each objective) significantly outperformed a unified critic design. The study used the Unitree G1 humanoid robot in simulation, training policies through a 13-level curriculum from stationary reaching to walking with moving targets. The findings suggest that architectural choices in multi-objective reinforcement learning may be more impactful than reward engineering for robot control efficiency.
A new arXiv preprint presents a controlled comparison of critic architectures for multi-objective reinforcement learning in humanoid robots, specifically examining whether to use a single unified critic or separate dual critics for coordinating locomotion and manipulation. Researchers trained policies on the Unitree G1 humanoid robot (23 active degrees of freedom) in NVIDIA Isaac Lab simulation, using a sequential curriculum spanning 13 levels from stationary reaching to walking with variable-orientation targets. Dual-critic policies substantially outperformed unified-critic approaches: reaching targets 3.5 times faster (6.5 vs. 22.6 simulation steps), achieving 2 times higher throughput (14.3 vs. 7.0 validated reaches per 1,000 steps), and attaining higher success rates (65.2% vs. 53.8%). Notably, additional anti-gaming reward mechanisms provided no further improvement, suggesting the architectural difference itself was the primary factor. The authors argue these results have implications for reinforcement learning fine-tuning of pre-trained policies, as unified critics may suppress learned manipulation behavior through competing locomotion gradients.
What's missing
The study's limitations and generalizability constraints are not detailed in the abstract. Specifically, it is unclear whether findings from simulation transfer to physical robots, whether results generalize beyond the Unitree G1 platform, what computational costs differ between architectures, or how sensitive results are to hyperparameter choices and curriculum design. The abstract does not discuss failure modes or discuss whether dual critics introduce other trade-offs such as increased training complexity or memory requirements.
What different sources said
- arXiv cs.LGCenter
Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation
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