Aug 2026· 2026 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)· pp. 1-5· 0 citations· 11 references
Abstract
This work presents a simulation-based validation framework for locomotion control on a custom-built 13 DoF bipedal robot using only signals derivable from a 6-axis inertial measurement unit (3D angular velocity and 3D gravity vector projection) as actor observations. The system employs the Genesis World simulator and the rsl-rl-lib library with PPO and a privileged critic architecture, where the actor only accesses IMU signals, reference speed commands, and past actions, without joint encoders or additional exteroceptive sensors. Training converges in 4,000 iterations (393M environment steps with 4,096 parallel environments): mean reward scales from 1.82 to 118.19, mean episode length goes from 22.8 to 1,007.6 steps, and success rate reaches 66.3%, with 99.8% vertical stability. The automatic curriculum unlocks running gait at iteration 126. This minimal instrumentation approach reduces hardware costs and facilitates replication in engineering laboratories with limited budgets.
High-dynamic quadruped jumping over steps and gap-like support interruptions remains difficult when external terrain perception is unavailable or unreliable. This paper studies a proprioception-only reinforcement-learning policy for bound jumping. The actor is initialized by a flat-ground bound strategy to obtain a sta...
Hong-Jing Huang, Cong Li, Tian-Wei Qian et al.· 2026 International Conferenc...· 0 citations
Payload forces must be accommodated during locomotion, while leash forces can specify desired motion. We investigate whether a shared three-dimensional force estimate in newtons, inferred from proprioceptive history under sustained loading, can support both tasks. An estimator and locomotion policy are jointly trained...
Run Wang, Xu Yang, Alapati Tuerxun et al.· 0 citations
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
Humanoid robots promise versatile mobility in cluttered, human-centric environments, but real deployment demands principled safety. Classical model-based gait generators yield interpretable motions but often lack the robustness and adaptability of modern reinforcement learning (RL) based approaches. We propose a model-...
We investigate humanoid locomotion with a fly-inspired recurrent controller and identify the pathways supporting its deployed behavior. The controller couples 3,609 continuous neural states to a simulated Unitree G1 through body-observation projections, a motor-neuron-labelled readout, and joint servos. We formulate th...
Isabel Guan, Yun-Tian Zhao, Ding-Yuan Zhang et al.· 0 citations
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