1 citations · 3 across the 20 of their papers we have counts for
20 papers
STRIDER: Stepping-Enabled Multi-Gait Hierarchical 3D Loco-Manipulation Framework for Humanoid Robots
Yuanzhuo Li, Wen Zhao, Zhe Yong +6
Humanoid loco-manipulation faces two prominent limitations: controllers using continuous velocity commands cannot precisely regulate individual footholds, while specialized foothol…
SOLO: Stable Omni-terrain Long-Horizon Perceptive Humanoid Locomotion
Pihai Sun, Gang Han, Jingkai Sun +17
Humans traverse complex terrain over long distances without losing balance, whereas perceptive humanoid policies become fragile as perception and control errors accumulate. We pres…
RobotPan: A 360 Surround-View Robotic Vision System for Embodied Perception
Jiahao Ma, Qiang Zhang, Peiran Liu +11
Surround-view perception is increasingly important for robotic navigation and loco-manipulation, especially in human-in-the-loop settings such as teleoperation, data collection, an…
Heracles: Bridging Precise Tracking and Generative Synthesis for General Humanoid Control
Zelin Tao, Zeran Su, Peiran Liu +13
Achieving general-purpose humanoid control requires a delicate balance between the precise execution of commanded motions and the flexible, anthropomorphic adaptability needed to r…
MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction
Qiang Zhang, Jiahao Ma, Peiran Liu +20
Humanoid motion control has witnessed significant breakthroughs in recent years, with deep reinforcement learning (RL) emerging as a primary catalyst for achieving complex, human-l…
DPL: Depth-only Perceptive Humanoid Locomotion via Realistic Depth Synthesis and Cross-Attention Terrain Reconstruction
Jingkai Sun, Gang Han, Pihai Sun +5
Recent advancements in legged robot perceptive locomotion have shown promising progress. However, terrain-aware humanoid locomotion remains largely constrained to two paradigms: de…
Compose Your Policies! Improving Diffusion-based or Flow-based Robot Policies via Test-time Distribution-level Composition
Jiahang Cao, Yize Huang, Hanzhong Guo +15
Diffusion-based models for robotic control, including vision-language-action (VLA) and vision-action (VA) policies, have demonstrated significant capabilities. Yet their advancemen…
Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots
Wei Cui, Haoyu Wang, Wenkang Qin +19
Humanoid robot technology is advancing rapidly, with manufacturers introducing diverse heterogeneous visual perception modules tailored to specific scenarios. Among various percept…
Occupancy World Model for Robots
Zhang Zhang, Qiang Zhang, Wei Cui +12
Understanding and forecasting the scene evolutions deeply affect the exploration and decision of embodied agents. While traditional methods simulate scene evolutions through trajec…
RoboOcc: Enhancing the Geometric and Semantic Scene Understanding for Robots
Zhang Zhang, Qiang Zhang, Wei Cui +7
3D occupancy prediction enables the robots to obtain spatial fine-grained geometry and semantics of the surrounding scene, and has become an essential task for embodied perception.…
ES-Parkour: Advanced Robot Parkour with Bio-inspired Event Camera and Spiking Neural Network
Qiang Zhang, Jiahang Cao, Jingkai Sun +5
In recent years, quadruped robotics has advanced significantly, particularly in perception and motion control via reinforcement learning, enabling complex motions in challenging en…
HumanoidPano: Hybrid Spherical Panoramic-LiDAR Cross-Modal Perception for Humanoid Robots
Qiang Zhang, Zhang Zhang, Wei Cui +13
The perceptual system design for humanoid robots poses unique challenges due to inherent structural constraints that cause severe self-occlusion and limited field-of-view (FOV). We…
LiPS: Large-Scale Humanoid Robot Reinforcement Learning with Parallel-Series Structures
Qiang Zhang, Gang Han, Jingkai Sun +7
In recent years, research on humanoid robots has garnered significant attention, particularly in reinforcement learning based control algorithms, which have achieved major breakthr…
Trinity: A Modular Humanoid Robot AI System
Jingkai Sun, Qiang Zhang, Gang Han +7
In recent years, research on humanoid robots has garnered increasing attention. With breakthroughs in various types of artificial intelligence algorithms, embodied intelligence, ex…
Distillation-PPO: A Novel Two-Stage Reinforcement Learning Framework for Humanoid Robot Perceptive Locomotion
Qiang Zhang, Gang Han, Jingkai Sun +6
In recent years, humanoid robots have garnered significant attention from both academia and industry due to their high adaptability to environments and human-like characteristics.…
E2H: A Two-Stage Non-Invasive Neural Signal Driven Humanoid Robotic Whole-Body Control Framework
Yiqun Duan, Qiang Zhang, Jinzhao Zhou +9
Recent advancements in humanoid robotics, including the integration of hierarchical reinforcement learning-based control and the utilization of LLM planning, have significantly enh…
Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models
Jiahang Cao, Qiang Zhang, Jingkai Sun +11
Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action traje…
Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning
Jiahang Cao, Qiang Zhang, Ziqing Wang +8
Sequential modeling has demonstrated remarkable capabilities in offline reinforcement learning (RL), with Decision Transformer (DT) being one of the most notable representatives, a…
Reinforcement Learning with Generalizable Gaussian Splatting
Jiaxu Wang, Qiang Zhang, Jingkai Sun +7
An excellent representation is crucial for reinforcement learning (RL) performance, especially in vision-based reinforcement learning tasks. The quality of the environment represen…
Whole-body Humanoid Robot Locomotion with Human Reference
Qiang Zhang, Peter Cui, David Yan +8
Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent c…