Push the Limit: Learning High-Speed Sprinting and Aggressive Cornering on a Full-Scale Humanoid
Abstract
We present a learning-based framework that enables a full-scale bipedal humanoid to sprint at over 7 m/s with motion-prior-guided gait and perform aggressive cornering at high speed with a pronounced trunk lean of up to $25^\circ$. Achieving this combination is challenging: above approximately 4 m/s, periodic foot impacts induce severe, high-frequency yaw-rate oscillations intrinsic to high-speed gaits. This high-frequency temporal structure corrupts the history of proprioceptive feedback that flat-MLP policies rely on, while rendering conventional yaw-rate tracking rewards counterproductive, as they fight against the robot's natural oscillatory dynamics. Moreover, a single style prior shared across skills cannot tell which skill is commanded and inherently compromises the low-speed skill fidelity. To overcome these limitations, we adopt a causal multi-head attention (MHA) module to explicitly model the high-frequency temporal feedback. In place of the oscillatory yaw rate, we design a heading-direction reward that supervises only the low-frequency velocity direction, allowing agile turning at high speed without suppressing natural gait oscillations. We further introduce skill-conditioned multi-adversarial-motion-prior (MAMP) discriminators that condition the style reward on the commanded skill. Ablation experiments confirm that each mechanism is essential: replacing structured temporal encoding with a flat-history MLP caps the top speed; reverting to yaw-rate tracking prevents high-speed turning; and a single shared discriminator makes in-place turning collapse into a quasi-static stance. Our framework bridges the gap between raw sprinting speed and reference-motion fidelity, demonstrating stable high-speed sprinting and aggressive cornering in both simulation and real-robot deployment.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a learning-based framework enabling full-scale humanoids to sprint at >7 m/s and perform aggressive cornering by introducing causal multi-head attention for high-frequency temporal modeling, heading-direction rewards to avoid suppressing natural gait oscillations, and skill-conditioned adversarial discriminators to maintain motion fidelity. This work significantly advances the state-of-the-art in high-speed bipedal locomotion by identifying and solving specific control challenges inherent to high-velocity gaits, bridging the gap between raw speed and reference-motion fidelity in both simulation and real-world deployment.
Methodology
The paper proposes a learning-based framework for high-speed bipedal locomotion, addressing specific failure modes of standard flat-MLP policies at high velocities. The core methodological contributions are threefold: (1) the use of a causal multi-head attention (MHA) module to explicitly model high-frequency temporal feedback, which is argued to be necessary because periodic foot impacts above 4 m/s induce yaw-rate oscillations that corrupt flat history buffers; (2) the design of a heading-direction reward that supervises only low-frequency velocity direction, replacing conventional yaw-rate tracking which is shown to be counterproductive at high speeds; and (3) the introduction of skill-conditioned multi-adversarial-motion-prior (MAMP) discriminators to maintain fidelity across different skills (sprinting vs. cornering). The ablation studies presented in the abstract confirm the necessity of each component, showing that flat MLPs cap top speed, yaw-rate tracking prevents turning, and shared discriminators degrade skill fidelity.
Results & evaluation
The evaluation includes both simulation (Gazebo) and real-robot deployment. The paper reports achieving sprinting speeds over 7 m/s and aggressive cornering with trunk lean up to 25 degrees at 5.5 m/s. The inclusion of real-robot validation is a significant strength, as high-speed bipedal control is notoriously difficult to transfer from simulation to reality. The ablation experiments are described as rigorous, isolating the impact of the temporal encoding, reward structure, and discriminator conditioning. The results demonstrate a clear capability gap closure between raw speed and motion fidelity.
Reproducibility
The paper is accepted to CoRL 2026, a venue known for rigorous standards. While the provided text is an abstract and figure caption, the mention of specific hyperparameters (e.g., 7 m/s, 25 degrees) and the use of standard simulation environments (Gazebo) suggest a level of detail typically found in CoRL papers. However, without the full code release or detailed hyperparameter tables in the provided snippet, full reproducibility cannot be guaranteed, though the methodology is clearly defined.
Limitations
The primary limitation is the reliance on a specific hardware platform (full-scale humanoid), which may limit immediate generalizability to other morphologies. Additionally, the high-speed control regime (>4 m/s) is inherently unstable and sensitive to environmental perturbations, which may not be fully explored in the provided abstract. The "causal MHA" approach may introduce computational overhead compared to flat MLPs, though this is likely negligible for onboard controllers.
Broader impact
This work pushes the physical limits of bipedal robotics, demonstrating that learning-based policies can handle the complex, high-frequency dynamics of sprinting. The insights regarding the failure of flat-MLP policies at high speeds and the necessity of explicit temporal modeling for high-frequency feedback are valuable for the broader robotics community. The skill-conditioned discriminator approach offers a generalizable technique for multi-skill learning in embodied agents. The paper presents a learning-based framework enabling full-scale humanoids to sprint at >7 m/s and perform aggressive cornering by introducing causal multi-head attention for high-frequency temporal modeling, heading-direction rewards to avoid suppressing natural gait oscillations, and skill-conditioned adversarial discriminators to maintain motion fidelity. This work significantly advances the state-of-the-art in high-speed bipedal locomotion by identifying and solving specific control challenges inherent to high-velocity gaits, bridging the gap between raw speed and reference-motion fidelity in both simulation and real-world deployment.
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