15 citations · 15 across the 10 of their papers we have counts for
12 papers
Beyond Kinematic Tracking: Preserving Expressive Characteristics in Humanoid Whole-Body Control
Lingfan Bao, Hanshang Zhu, Yan Pan +6
Whole-body control plays a crucial role in enabling humanoid robots to execute expressive motions. Faithful execution requires preserving both the intended action and its expressiv…
Reliability-Aware Sparse Route Memory for Round-Trip Vision-Language Navigation
Bojun Long, Lingfan Bao, Tianhu Peng +2
Vision-language navigation (VLN) is typically evaluated as a one-way task, although deployed robots may need to return after reaching a goal. We study continuous round-trip VLN and…
An Empirical Study and Open Testbed for Federated Fine-Tuning of Vision-Language-Action Models
Zhekai Duan, Kevin Ziyang Xie, Xinyu Tan +5
Adapting a pretrained Vision-Language-Action (VLA) model to a new robot, environment, or task requires demonstrations that are collected locally and often discarded. Federated lear…
PAMoR: Parameterized Affective Motion Generation in Real Time for Humanoid Robots
Yan Pan, Lingfan Bao, Tianhu Peng +1
People read a humanoid robot's motion in social settings not only for the action performed but for the affect conveyed. Motion carrying that affect has so far been generated for hu…
Quadruped Parkour Learning: Sparsely Gated Mixture of Experts with Visual Input
Michael Ziegltrum, Jianhao Jiao, Tianhu Peng +2
Robotic parkour provides a compelling benchmark for advancing locomotion over highly challenging terrain, including large discontinuities such as elevated steps. Recent approaches…
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…
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…
Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion
Joseph Humphreys, Chengxu Zhou
Legged robots must adapt their gait to navigate unpredictable environments, a challenge that animals master with ease. However, most deep reinforcement learning (DRL) approaches to…
Learning Bipedal Walking on a Quadruped Robot via Adversarial Motion Priors
Tianhu Peng, Lingfan Bao, Joseph Humphreys +3
Previous studies have successfully demonstrated agile and robust locomotion in challenging terrains for quadrupedal robots. However, the bipedal locomotion mode for quadruped robot…
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…