MAPO: Mixture of Experts with Alternating Policy Optimization for Universal Humanoid Motion Tracking
Overview
This research paper proposes MAPO, a method combining mixture-of-experts policies with alternating optimization to address divergence misestimation issues. The authors claim that MAPO successfully tracks 182 motions where PPO with MoE fails, achieving the highest tracking success rate and lowest error among compared methods.
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Reported quantity · trajectories: 182 other · Basis not reported
“s the router and the experts to avoid the divergence misestimation. MAPO consistently tracks 182 motions that PPO with MoE fails to track, whereas PPO with MoE tracks only one motion that MAPO fails to track. MAPO also attains the highest tracking success rate among the compared methods and the lowest tracking error across most error metrics, demonstrating its superior performance. Videos can be found on the project”
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Reported quantity · robots: 1 other · Basis not reported
“s the router and the experts to avoid the divergence misestimation. MAPO consistently tracks 182 motions that PPO with MoE fails to track, whereas PPO with MoE tracks only one motion that MAPO fails to track. MAPO also attains the highest tracking success rate among the compared methods and the lowest tracking error across most error metrics, demonstrating its superior performance. Videos can be found on the project”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalMAPO: Mixture of Experts with Alternating Policy Optimization for Universal Humanoid Motion Tracking
This research paper proposes MAPO, a method combining mixture-of-experts policies with alternating optimization to address divergence misestimation issues. The authors claim that MAPO successfully tracks 182 motions where PPO with MoE fails, achieving the highest tracking success rate and lowest error among compared methods.
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