8 papers
Tac4Loco: Learning Spatiotemporal Plantar Pressure Representations for Humanoid Locomotion
Ziyun Liu, Sikai Guo, Zheng Li +6
Humanoid robots are expected to traverse complex terrains, where the plantar support may vary dramatically due to foot placement errors, ground properties, and transient dynamics.…
FT-WBC: Learning Fault-Tolerant Whole-Body Control for Legged Loco-Manipulation
Yudong Zhong, Pengfei Mai, Sikai Guo +6
Legged manipulators combine the mobility of legged platforms with the manipulation capability of robotic arms. However, arm-induced Center-of-Mass shifts and dynamic disturbances m…
MoE-ACT: Scaling Multi-Task Bimanual Manipulation with Sparse Task-Conditioned Mixture-of-Experts Transformers
Kangjun Guo, Haichao Liu, Yanji Sun +3
Developing a unified policy for multi-task robotic manipulation remains challenging due to policy degradation from task interference and negative transfer. In this work, we propose…
OmniDP: Beyond-FOV Large-Workspace Humanoid Manipulation with Omnidirectional 3D Perception
Pei Qu, Zheng Li, Yufei Jia +5
The deployment of humanoid robots for dexterous manipulation in unstructured environments remains challenging due to perceptual limitations that constrain the effective workspace.…
SpecFuse: A Spectral-Temporal Fusion Predictive Control Framework for UAV Landing on Oscillating Marine Platforms
Haichao Liu, Yufeng Hu, Shuang Wang +3
Autonomous landing of Uncrewed Aerial Vehicles (UAVs) on oscillating marine platforms is severely constrained by wave-induced multi-frequency oscillations, wind disturbances, and p…
Embracing Bulky Objects with Humanoid Robots: Whole-Body Manipulation with Reinforcement Learning
Chunxin Zheng, Kai Chen, Zhihai Bi +5
Whole-body manipulation (WBM) for humanoid robots presents a promising approach for executing embracing tasks involving bulky objects, where traditional grasping relying on end-eff…
GUIDE: A Diffusion-Based Autonomous Robot Exploration Framework Using Global Graph Inference
Zijun Che, Yinghong Zhang, Shengyi Liang +3
Autonomous exploration in structured and complex indoor environments remains a challenging task, as existing methods often struggle to appropriately model unobserved space and plan…
ManiVID-3D: Generalizable View-Invariant Reinforcement Learning for Robotic Manipulation via Disentangled 3D Representations
Zheng Li, Pei Qu, Yufei Jia +6
Deploying visual reinforcement learning (RL) policies in real-world manipulation is often hindered by camera viewpoint changes. A policy trained from a fixed front-facing camera ma…