6 papers
A High-Fidelity Digital Twin for Robotic Manipulation Based on 3D Gaussian Splatting
Ziyang Sun, Lingfan Bao, Tianhu Peng +2
Developing high-fidelity, interactive digital twins is crucial for enabling closed-loop motion planning and reliable real-world robot execution, which are essential to advancing si…
SCDP: Learning Humanoid Locomotion from Partial Observations via Mixed-Observation Distillation
Milo Carroll, Tianhu Peng, Lingfan Bao +2
Distilling humanoid locomotion control from offline datasets into deployable policies remains a challenge, as existing methods rely on privileged full-body states that require comp…
Deep Reinforcement Learning for Bipedal Locomotion: A Brief Survey
Lingfan Bao, Joseph Humphreys, Tianhu Peng +1
Bipedal robots are gaining global recognition due to their potential applications and advancements in artificial intelligence, particularly through Deep Reinforcement Learning (DRL…
Sim-to-Real Transfer in Deep Reinforcement Learning for Bipedal Locomotion
Lingfan Bao, Tianhu Peng, Chengxu Zhou
This chapter addresses the critical challenge of simulation-to-reality (sim-to-real) transfer for deep reinforcement learning (DRL) in bipedal locomotion. After contextualizing the…
Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Lingfan Bao, Yan Pan, Tianhu Peng +2
Effective human-robot interaction requires robots to identify human intentions and generate expressive, socially appropriate motions in real-time. Existing approaches often rely on…
Gait-Conditioned Reinforcement Learning with Multi-Phase Curriculum for Humanoid Locomotion
Tianhu Peng, Lingfan Bao, Chengxu Zhou
We present a unified gait-conditioned reinforcement learning framework that enables humanoid robots to perform standing, walking, running, and smooth transitions within a single re…