Extreme-RGMT is introduced, a two-stage continual learning framework for robust generalist humanoid control that achieves state-of-the-art generalist whole-body motion-tracking performance, including substantially improved completion of challenging highly dynamic motions.
Abstract
Humans can progressively acquire highly dynamic motor skills while preserving reliable everyday motor abilities. In contrast, existing humanoid controllers face a trade-off between generalist and specialist capabilities: generalist motion tracking policies struggle to reliably execute rare highly dynamic motions, whereas specialist training can degrade previously acquired behaviors. We introduce Extreme-RGMT, a two-stage continual learning framework for robust generalist humanoid control. The method first learns a generalist motion-tracking base policy from diverse multi-source motion data, then employs an asymmetric skill acquisition and capability consolidation mechanism to constrain policy drift on mastered motions while emphasizing difficult dynamic segments. To address the scarcity of highly dynamic motions, their high failure rates, and the resulting shortage of informative samples, Extreme-RGMT combines difficulty-aware sampling with advantage-prioritized trajectory resampling to emphasize critical segments. Experiments show that Extreme-RGMT achieves state-of-the-art generalist whole-body motion-tracking performance, including substantially improved completion of challenging highly dynamic motions. The resulting controller directly executes diverse unseen highly dynamic motions under fixed references and online inertial motion-capture inputs, advancing generalist whole-body motion-tracking controllers toward highly dynamic motor capabilities at the human-expert level.
Athena-WBC is proposed, a compact teacher-student pipeline with capability-aligned policy experts for long-tail humanoid whole-body control that shows improved recovery of training-set long-tail motions and better held-out tracking than a strong SONIC-recipe baseline, using only a small number of experts.
Yuanchang Jiang, Ningyuan Zhang, Xicun Yang et al.· 1 citation
StableMimic is presented, a unified tracker trained beyond the nominal tracking distribution that achieves the lowest errors on all four tracking metrics among five methods and attains the lowest values on six of seven post-fall motion and load measures, supporting improved interaction safety under this protocol.
Weihao Wu, Mingzhe Huang, Ruofei Liu et al.· 0 citations
Learning to execute complex, multi-stage tasks requires skill representations that are both compositionally stable and adaptive in execution. Existing hierarchical approaches often face a fundamental trade-off: continuous skills suffer from representational drift due to unconstrained embedding boundaries, while discrete skills exhibit limited expressivity because their deterministic selection cannot capture the multi-modal nuances required for adaptive execution. This tension makes it difficult to achieve reliable composition and adaptive control within a single framework. To address this, we propose DualSkill, a hierarchical framework that learns stable hard skill primitives and builds adaptive soft skills from them. Specifically, DualSkill acquires discrete hard skills via vector quantization with motion-aware distillation, yielding robust and reusable motion primitives that provide structural anchors for skill composition. Conditioned on these primitives, soft skills are modeled as probabilistic continuous mixtures that adapt skill execution while preserving temporal consistency. DualSkill then predicts future skill intentions autoregressively and decodes them into precise low-level actions. We support DualSkill with both theoretical guarantees on its skill representation and extensive experiments across diverse simulation benchmarks and a real-world robotic platform, showing that it outperforms strong baselines and improves generalization. Note to Practitioners—This paper was motivated by the need for robots to execute complex, multi-step tasks in dynamic environments such as homes, warehouses, and factories. In practice, control systems often struggle to balance modular, reusable skills with smooth transitions, leading to unstable or inefficient behavior when task conditions change. Existing approaches typically force a trade-off: either continuous skills that suffer from representational drift or discrete libraries that result in inflexible behavior. This paper presents DualSkill, a hierarchical framework that bridges this gap by decomposing behaviors into stable hard skills for structural reliability and adaptive soft skills for smooth execution. We validate that DualSkill significantly reduces failure rates in complex manipulation tasks on both simulated benchmarks and physical robots. However, the system still relies on structured training data, which may limit its initial deployment in highly unstructured environments. In the future, DualSkill could be applied to mobile robots and human-robot collaboration, further leveraging its flexible and robust framework for real-world tasks.
Ziru Wang, Long Qian, Haowen Sun et al.· IEEE Transactions on Automat...· 0 citations
Humanoid general motion tracking requires policies that can follow diverse whole-body references while maintaining balance. Building such policies involves many practical design choices, and their individual effects are often hard to assess. We address this issue with an empirical study of common modeling and training factors used in recent humanoid motion-imitation pipelines. To make the study controlled and reproducible, we developed YAHMP, an open-source modular framework for training, evaluating, and deploying whole-body motion tracking policies on the Unitree G1. Within YAHMP, we define a nominal configuration and compare variants that differ in motion-command representation, observation history, action representation, actuation profile, hand-force randomization during training, and training approach. We evaluate the resulting policies on a test set of retargeted human motions and compare the nominal policy with TWIST2 as an external baseline trained on the same motion set. The results distinguish choices with clear tracking effects from choices that mainly change actuation effort, training complexity, or physical interaction capability. Finally, we deploy YAHMP policies zero-shot on the real Unitree G1, demonstrating diverse whole-body motion tracking, balance under external perturbations, and forceful interaction.
A three-stage pipeline that turns motion-imitation skills into a reusable hybrid motion prior (HMP) for humanoid locomotion and shows that training the codebook with the rotation trick improves latent organization and reduces downstream falls compared with a standard straight-through estimator.
LooperMuscle is introduced, a composed expert policy learning framework that restores tracking quality while preserving high training efficiency, and substantially outperforms vanilla FastSAC in motion tracking accuracy while requiring far less wall-clock time than PPO.
Boyi Liu, Qijing Li, Tianqi Yu et al.· 0 citations