Agile asymmetric multi-legged locomotion: contact planning via geometric mechanics and spin model duality
- URL: http://arxiv.org/abs/2602.09123v1
- Date: Mon, 09 Feb 2026 19:13:14 GMT
- Title: Agile asymmetric multi-legged locomotion: contact planning via geometric mechanics and spin model duality
- Abstract summary: We develop a principled framework for discovering new control structures in multi-legged locomotion.<n>We use geometric mechanics to reduce contact-rich locomotion planning to a graph optimization problem.<n>We identify an asymmetric locomotion strategy for a hexapod robot that achieves a forward speed of 0.61 body lengths per cycle.
- Score: 15.47009589379934
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Legged robot research is presently focused on bipedal or quadrupedal robots, despite capabilities to build robots with many more legs to potentially improve locomotion performance. This imbalance is not necessarily due to hardware limitations, but rather to the absence of principled control frameworks that explain when and how additional legs improve locomotion performance. In multi-legged systems, coordinating many simultaneous contacts introduces a severe curse of dimensionality that challenges existing modeling and control approaches. As an alternative, multi-legged robots are typically controlled using low-dimensional gaits originally developed for bipeds or quadrupeds. These strategies fail to exploit the new symmetries and control opportunities that emerge in higher-dimensional systems. In this work, we develop a principled framework for discovering new control structures in multi-legged locomotion. We use geometric mechanics to reduce contact-rich locomotion planning to a graph optimization problem, and propose a spin model duality framework from statistical mechanics to exploit symmetry breaking and guide optimal gait reorganization. Using this approach, we identify an asymmetric locomotion strategy for a hexapod robot that achieves a forward speed of 0.61 body lengths per cycle (a 50% improvement over conventional gaits). The resulting asymmetry appears at both the control and hardware levels. At the control level, the body orientation oscillates asymmetrically between fast clockwise and slow counterclockwise turning phases for forward locomotion. At the hardware level, two legs on the same side remain unactuated and can be replaced with rigid parts without degrading performance. Numerical simulations and robophysical experiments validate the framework and reveal novel locomotion behaviors that emerge from symmetry reforming in high-dimensional embodied systems.
Related papers
- Exploring Nonlinear Body Oscillations for Natural Quadruped Gaits [5.326551724407955]
Animals' body morphology shapes the gait patterns they can perform, where mechanical resonance reduces the need for active control.<n>Most quadruped robots are not specifically designed to exploit mechanical resonance due to the complexity of nonlinear dynamics.<n>We present a proof of concept framework making the nonlinear dynamics of a robot predictable in the design process.
arXiv Detail & Related papers (2026-09-01T01:25:11Z) - Motion Design for Grasp-Based Dynamic Locomotion in Microgravity [5.301721673195314]
This paper presents insights for grasp-based dynamic locomotion with multi-limbed robotic systems in microgravity.<n>The investigated design parameters include gait pattern, stride length, locomotion speed, and nominal posture.<n>The results demonstrate that enlarging the feasible contact wrench space and attenuating impulsive whole-body dynamics improve locomotion performance.
arXiv Detail & Related papers (2026-05-20T20:06:01Z) - A 26-Gram Butterfly-Inspired Robot Achieving Autonomous Tailless Flight [61.21669716392821]
textitAirPulse is a 26-gram butterfly-inspired robot that achieves the first onboard, closed-loop controlled flight for a tailless two-winged platform at this scale.<n>It replicates key biomechanical traits of butterfly flight, utilizing low-aspect-ratio, compliant carbon-fiber-reinforced wings and low-frequency flapping that reproduces characteristic biological body undulations.
arXiv Detail & Related papers (2026-02-06T15:58:50Z) - TriphiBot: A Triphibious Robot Combining FOC-based Propulsion with Eccentric Design [9.930529399697557]
Triphibious robots capable of multi-domain motion are promising to handle complex tasks across diverse environments.<n>Existing designs primarily focus on dual-mode platforms, and some designs suffer from high mechanical complexity or low propulsion efficiency.<n>We propose a novel triphibious robot capable of aerial, terrestrial, and aquatic motion, by a minimalist design combining a quadcopter structure with two passive wheels.
arXiv Detail & Related papers (2026-02-01T18:38:22Z) - PALo: Learning Posture-Aware Locomotion for Quadruped Robots [29.582249837902427]
We propose an end-to-end deep reinforcement learning framework for posture-aware locomotion named PALo.<n> PALo handles simultaneous linear and angular velocity tracking and real-time adjustments of body height, pitch, and roll angles.<n> PALo achieves agile posture-aware locomotion control in simulated environments and successfully transfers to real-world settings without fine-tuning.
arXiv Detail & Related papers (2025-03-06T14:13:59Z) - Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
arXiv Detail & Related papers (2025-02-24T14:53:45Z) - 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) - Simultaneous Contact-Rich Grasping and Locomotion via Distributed
Optimization Enabling Free-Climbing for Multi-Limbed Robots [60.06216976204385]
We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems.
We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
arXiv Detail & Related papers (2022-07-04T13:52:10Z) - 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) - GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with
a Centroidal Model [18.66472547798549]
We show how model-free reinforcement learning can be effectively used with a centroidal model to generate robust control policies for quadrupedal locomotion.
We show the potential of the method by demonstrating stepping-stone locomotion, two-legged in-place balance, balance beam locomotion, and sim-to-real transfer without further adaptations.
arXiv Detail & Related papers (2021-04-20T05:55:13Z) - Reinforcement Learning for Robust Parameterized Locomotion Control of
Bipedal Robots [121.42930679076574]
We present a model-free reinforcement learning framework for training robust locomotion policies in simulation.
domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics.
We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw.
arXiv Detail & Related papers (2021-03-26T07:14:01Z)
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.