Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-8· 0 citations· 27 references
Abstract
This paper explores the Underactuated Virtual Gravity (UVG) controller, a model-based control approach designed to achieve energy-efficient bipedal walking. The UVG controller minimizes actuator effort during level-ground walking by capitalizing on the inherent dynamics that facilitate stable passive gaits on downward slopes. By effectively leveraging torso dynamics to support the application of the UVG, the method turns the innate underactuation of bipedal systems into an advantage. Extensive benchmarking against state-of-the-art methods such as the Trajectory Optimization for trajectory planning combined with Non-linear Model Predictive Control for tracking shows that the UVG achieves superior energy efficiency within its effective range while constituting a closed-form controller that requires fewer computational resources than numerical methods. The results highlight the UVG and related dynamics-based approaches as a compelling option for energy-conscious, task-focused robot designs.
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
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
Learning energy-efficient humanoid locomotion requires discovering mechanically economical gait coordination, not merely reducing actuator effort. Reinforcement learning promotes efficiency through effort-related reward penalties, which guide the step-to-step mechanics of walking only indirectly. This article proposes...
Hyeonjin Choi, Joongheon Kim, Daekyum Kim· 0 citations
This paper presents a proprioception-based rolling control framework for wheeled-quadruped robots. The proposed framework serves as a reactive control approach for adaptive terrain traversal and body stabilization, without explicit motion planning. For wheeled-quadruped robots, adding wheels significantly increases t...
Shi-Chao Zhou, Zhong-Qu Xie, Ling-Kun Chen et al.· Proceedings of the Instituti...· 0 citations
Humanoid robots could transport payloads substantially heavier than themselves by pulling passive wheeled vehicles instead of carrying the load. This capability, however, creates a coupled locomotion problem: the robot must maintain persistent upper-body contact while adapting to unknown, configuration-dependent forces...
Yang-Zhi Yang, Xian-Sheng Lin, Zhao-Ming Xie et al.· 0 citations
A bipedal robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated platforms such as the biped considered here, which has four actuated joints per leg and no hip or ankle roll. This paper presents a Hierarchical Reinforcement Learning (HRL...
J. Sahoo, Saurabh Kumar, Surya Prakash S.K. et al.· 0 citations
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