Breaking speed scaling in quadrupedal robots via Huygens' coupled-pendulum dynamics
- URL: http://arxiv.org/abs/2609.13290v1
- Date: Wed, 09 Sep 2026 10:58:20 GMT
- Title: Breaking speed scaling in quadrupedal robots via Huygens' coupled-pendulum dynamics
- Abstract summary: We show that superior locomotion can emerge from principled exploitation of intrinsic dynamics rather than brute-force hardware scaling.<n>A quadruped robot achieves a running speed of 10.74 m/s (Froude number 21.4) and completes a 100-meter sprint in 12.2 seconds.<n>These results establish inertial coupling as an underlying mechanism governing high-speed legged locomotion and highlight its role in reducing force requirements.
- Score: 3.208498459001999
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Achieving biological-level running speeds has largely been pursued through advances in control algorithms, which improve the utilization of existing hardware. However, the ultimate speed limits remain governed by the underlying force and torque requirements of rapid locomotion, which are typically addressed through increased actuator capacity. Inspired by Huygens' coupled pendulums, we demonstrate that superior locomotion can emerge from principled exploitation of intrinsic dynamics rather than brute-force hardware scaling. Inter-limb inertial coupling redistributes energy across the gait cycle and reduces peak joint torque required for rapid periodic motion, thereby expanding the achievable speed without proportional increases in actuator capability. Incorporating hardware parameters as additional design variables further extends this analysis into a co-optimization framework, enabling the systematic utilization of inertial coupling in robot design. Guided by this framework, a quadruped robot achieves a running speed of 10.74 m/s (Froude number 21.4) and completes a 100-meter sprint in 12.2 seconds, representing the first legged robot to surpass 10 m/s. These results establish inertial coupling as an underlying mechanism governing high-speed legged locomotion and highlight its role in reducing force requirements, offering new insights into the design of agile robotic systems.
Related papers
- Extending the Speed Limit of Quadrupedal Locomotion via Refined Actuator Modeling and Adaptive Command Scheduling [0.9298381080776307]
We propose a high-speed locomotion framework that reduces sim-to-real discrepancies and stabilizes learning over a wide command distribution.<n>A refined actuator model explicitly captures high-speed voltage coupling and magnetic saturation, enabling a more accurate representation of the torque-speed envelope.<n>Experiments on the 36.5 kg quadruped BlackPanther2 demonstrate speeds of up to 13.2 m/s on a treadmill and 11.65 m/s outdoors, establishing a new state-of-the-art and, to the best of our knowledge, a world record for quadrupedal robot locomotion.
arXiv Detail & Related papers (2026-09-09T08:45:38Z) - MobileVLA-R1 2.0: RL-Enhanced Reasoning for Mobile Robot Control [63.85634404397772]
MobileVLA-R1 2.0 is an RL-enhanced vision-language-action framework for mobile robots.<n>It learns multi-granularity reasoning over embodied trajectories through supervised Chain-of-Thought (CoT) alignment and reinforcement learning.<n>We conduct evaluations on language-guided navigation, quadruped control, and humanoid mobile manipulation, covering VLN-CE, QUARD, and real-world deployments on Unitree Go2 and G1 robots.
arXiv Detail & Related papers (2026-09-05T20:31:14Z) - Time-Division Multiplexing Actuation in Tendon-Driven Arms: Lightweight Design and Fault Tolerance [14.371753469050942]
Time-Division Multiplexing Actuation (TDMA) is a practical approach for tendon-driven robots.<n>MuxArm achieves a self-weight of 2.17 kg, supports an actuator driving capacity of 10 kg, and maintains end-effector accuracy up to 1% of its length.
arXiv Detail & Related papers (2026-04-18T07:27:45Z) - A Robotic Testing Platform for Pipelined Discovery of Resilient Soft Actuators [63.07587517846448]
We propose an optimization pipeline enabled by a novel testing robot capable of scanning DEA lifetime.<n>The robot integrates electro-mechanical property measurement, programmable voltage input, and multi-channel testing capacity.<n>Using it, we scanned the lifetime of Elastosil-based linear actuators across parameters including input voltage magnitude, frequency, electrode material concentration, and electrical connection filler.
arXiv Detail & Related papers (2026-02-24T14:41:28Z) - Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning [20.562583114049747]
Co-jump is a cooperative task where two quadrupedal robots synchronize to execute jumps far beyond their solo capabilities.<n>We tackle the high-impulse contact dynamics of this task under a decentralized setting, achieving synchronization without explicit communication or pre-specified motion primitives.<n>One of the robots achieves a foot-end elevation of 1.1 m, which represents a 144% improvement over the 0.45 m jump height of a standalone quadrupedal robot.
arXiv Detail & Related papers (2026-02-11T04:28:04Z) - FastStair: Learning to Run Up Stairs with Humanoid Robots [19.796528911795516]
Running up stairs is effortless for humans but difficult for humanoid robots.<n>FastStair is a planner-guided, multi-stage learning framework to achieve fast and stable stair ascent.<n>We deploy the resulting controller on the Oli humanoid robot, achieving stable stair ascent at commanded speeds up to 1.65 m/s.
arXiv Detail & Related papers (2026-01-15T13:14:59Z) - Antagonistic Bowden-Cable Actuation of a Lightweight Robotic Hand: Toward Dexterous Manipulation for Payload Constrained Humanoids [4.320393382724067]
We present a lightweight anthropomorphic hand actuated by Bowden cables.<n>The design substantially reduces distal mass while maintaining anthropomorphic scale and dexterity.<n>Using the proposed methods, the hand assembly with a distal mass of 236g demonstrated reliable execution of dexterous tasks.
arXiv Detail & Related papers (2025-12-31T06:07:11Z) - RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking [50.200035833530876]
RobotDancing is a simple, scalable framework that predicts residual joint targets to explicitly correct dynamics discrepancies.<n>It can track multi-minute, high-energy behaviors (jumps, spins, cartwheels) and deploys zero-shot to hardware with high motion tracking quality.
arXiv Detail & Related papers (2025-09-25T03:30:34Z) - Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots.
We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing.
This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
arXiv Detail & Related papers (2024-01-30T10:48:43Z) - VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait
Representation [78.92147339883137]
We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait.
We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration.
The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
arXiv Detail & Related papers (2022-05-02T19:49:53Z) - Next Steps: Learning a Disentangled Gait Representation for Versatile
Quadruped Locomotion [69.87112582900363]
Current planners are unable to vary key gait parameters continuously while the robot is in motion.
In this work we address this limitation by learning a latent space capturing the key stance phases constituting a particular gait.
We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, foot step height and full stance duration.
arXiv Detail & Related papers (2021-12-09T10:02:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.