Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 684-689· 0 citations· 20 references
Abstract
This research proposes a novel control architecture for dynamically stable ultra-slow quadruped locomotion on unstructured and slippery terrains. The approach is based on Imitation Learning (IL), where two neural controllers are trained to imitate an optimal Quadratic Programming (QP) controller derived from a Linear Time Invariant (LTI) Variation-Based Linearized (VBL) model of the robot. Stability and safety are incorporated through Lyapunov-inspired and Linear Matrix Inequality (LMI) constraints integrated into the training process. A switching strategy between leg pairs enables continuous balance during slow gait execution. Preliminary simulation results demonstrate accurate center-of-mass tracking and stable behavior across gait phases, supporting the feasibility of the proposed method for real-world deployment.
A refined actuator model explicitly captures high-speed voltage coupling and magnetic saturation, enabling a more accurate representation of the torque–speed envelope and a reinforcement learning framework incorporating a two-stage curriculum and adaptive command scheduling (ACS) ensures stable training.
Yu-Cheng Tao, Shao-Wen Cheng, Guo-Rong Lan et al.· IEEE Robotics and Automation...· 0 citations
: Stable high-speed locomotion of legged robotic platforms depends on gait parameters that are usually tuned by hand, with no guarantee that the resulting cycle is stable or efficient. This paper builds a hybrid dynamical model of the center-of-mass trajectory using a spring-loaded inverted pendulum abstraction, with s...
A deep reinforcement learning approach for fault-tolerant locomotion under actuator power loss that employs an asymmetric actor-critic architecture in which the critic has access to privileged information during training, while the actor learns to reconstruct a corresponding latent representation from proprioceptive ob...
Giovanbattista Gravina, Luca Rossini, Carlo Rizzardo et al.· 1 citation
Experimental results demonstrate that the integrated system improves locomotion stability, energy efficiency, and terrain adaptability compared with baseline controllers, highlighting the effectiveness of combining a structured gait prior, lightweight residual coordination, and hardware-aware deployment for practical q...
Model Predictive Control is an effective tool for robust humanoid gait generation in complex 3D environments. This challenging operating condition typically results in non-convex footstep constraints which, if directly included in the optimization problem, would introduce significant computation overhead. We present a...
Andrew S. Habib, Nicola Scianca, L. Lanari et al.· IEEE Robotics and Automation...· 0 citations
This paper analyzes prevailing challenges and corresponding countermeasures regarding hardware deployment, sample efficiency, and model generalization capacity, and points out that further integration of multi-algorithms, optimization of sim-to-real transformation and overall strategy design will be the main trends in...
Yuchen Fan· MATEC Web of Conferences· 0 citations
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