Huiqiao Fu

Agile Hauler Curriculum: Learning High-Speed Locomotion for Robots under Demanding Payloads

DEAL: Diffusion Evolution Adversarial Learning for Sim-to-Real Transfer

Training Reinforcement Learning (RL) controllers in simulation offers cost-efficiency and safety advantages. However, the resultant policies often suffer significant performance degradation during real-world deployment due to the reality gap. Previous works like System Identification (Sys-Id) have a…

PN-GAIL: Leveraging Non-optimal Information from Imperfect Demonstrations

Imitation learning aims at constructing an optimal policy by emulating expert demonstrations. However, the prevailing approaches in this domain typically presume that the demonstrations are optimal, an assumption that seldom holds true in the complexities of real-world applications. The data collect…

EASI: Evolutionary Adversarial Simulator Identification for Sim-to-Real Transfer

Reinforcement Learning (RL) controllers have demonstrated remarkable performance in complex robot control tasks. However, the presence of reality gap often leads to poor performance when deploying policies trained in simulation directly onto real robots. Previous sim-to-real algorithms like Domain R…

Multi-agent Reinforcement Learning with Hybrid Action Space for Free Gait Motion Planning of Hexapod Robots

Legged robots are able to overcome challenging terrains through diverse gaits formed by contact sequences. However, environments characterized by discrete footholds present significant challenges. In this paper, we tackle the problem of free gait motion planning for hexapod robots walking in randoml…

Ess-InfoGAIL: Semi-supervised Imitation Learning from Imbalanced Demonstrations

Imitation learning aims to reproduce expert behaviors without relying on an explicit reward signal. However, real-world demonstrations often present challenges, such as multi-modal, data imbalance, and expensive labeling processes. In this work, we propose a novel semi-supervised imitation learning…

Deep Reinforcement Learning for Multi-contact Motion Planning of Hexapod Robots

Legged locomotion in a complex environment requires careful planning of the footholds of legged robots. In this paper, a novel Deep Reinforcement Learning (DRL) method is proposed to implement multi-contact motion planning for hexapod robots moving on uneven plum-blossom piles. First, the motion of…

Learning to Navigate in a VUCA Environment: Hierarchical Multi-expert Approach

Despite decades of efforts, robot navigation in a real scenario with volatility, uncertainty, complexity, and ambiguity (VUCA for short), remains a challenging topic. Inspired by the central nervous system (CNS), we propose a hierarchical multi-expert learning framework for autonomous navigation in…