1 citations · 4 across the 21 of their papers we have counts for
22 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.…
UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning
Haoyuan Deng, Yitong Gao, Yudong Lin +3
Human-in-the-loop reinforcement learning (HiL-RL) has emerged as an effective paradigm for real-world robotic manipulation, enabling online policy improvement with human guidance.…
RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation
Haichao Liu, Yuheng Zhou, Zhenyu Wu +14
Embodied Artificial Intelligence (EAI) is rapidly developing, gradually subverting previous autonomous systems' paradigms from isolated perception to integrated, continuous action.…
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…
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…
OmniScene: Attention-Augmented Multimodal 4D Scene Understanding for Autonomous Driving
Pei Liu, Hongliang Lu, Haichao Liu +5
Human vision is capable of transforming two-dimensional observations into an egocentric three-dimensional scene understanding, which underpins the ability to translate complex scen…
An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment
Xiaoyun Qiu, Haichao Liu, Yue Pan +2
In mixed-traffic environments, autonomous vehicles (AVs) must interact with heterogeneous human-driven vehicles (HVs) whose intentions and driving styles vary across individuals an…
VLM-UDMC: VLM-Enhanced Unified Decision-Making and Motion Control for Urban Autonomous Driving
Haichao Liu, Haoren Guo, Pei Liu +4
Scene understanding and risk-aware attentions are crucial for human drivers to make safe and effective driving decisions. To imitate this cognitive ability in urban autonomous driv…
RoboDexVLM: Visual Language Model-Enabled Task Planning and Motion Control for Dexterous Robot Manipulation
Haichao Liu, Sikai Guo, Pengfei Mai +3
This paper introduces RoboDexVLM, an innovative framework for robot task planning and grasp detection tailored for a collaborative manipulator equipped with a dexterous hand. Previ…
VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion
Pei Liu, Haipeng Liu, Haichao Liu +3
Human drivers adeptly navigate complex scenarios by utilizing rich attentional semantics, but the current autonomous systems struggle to replicate this ability, as they often lose…
CoDriveVLM: VLM-Enhanced Urban Cooperative Dispatching and Motion Planning for Future Autonomous Mobility on Demand Systems
Haichao Liu, Ruoyu Yao, Wenru Liu +3
The increasing demand for flexible and efficient urban transportation solutions has spotlighted the limitations of traditional Demand Responsive Transport (DRT) systems, particular…
UDMC: Unified Decision-Making and Control Framework for Urban Autonomous Driving with Motion Prediction of Traffic Participants
Haichao Liu, Kai Chen, Yulin Li +3
Current autonomous driving systems often struggle to balance decision-making and motion control while ensuring safety and traffic rule compliance, especially in complex urban envir…
CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model
Ruoyu Yao, Yubin Wang, Haichao Liu +4
Decision-making and motion planning constitute critical components for ensuring the safety and efficiency of autonomous vehicles (AVs). Existing methodologies typically adopt two p…
Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles
Wenru Liu, Haichao Liu, Lei Zheng +2
This paper introduces a local planner that synergizes the decision making and trajectory planning modules towards autonomous driving. The decision making and trajectory planning ta…
LMMCoDrive: Cooperative Driving with Large Multimodal Model
Haichao Liu, Ruoyu Yao, Zhenmin Huang +2
To address the intricate challenges of decentralized cooperative scheduling and motion planning in Autonomous Mobility-on-Demand (AMoD) systems, this paper introduces LMMCoDrive, a…
Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving
Hang Zhou, Haichao Liu, Hongliang Lu +3
Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction…
Robot Navigation in Unknown and Cluttered Workspace with Dynamical System Modulation in Starshaped Roadmap
Kai Chen, Haichao Liu, Yulin Li +3
Compared to conventional decomposition methods that use ellipses or polygons to represent free space, starshaped representation can better capture the natural distribution of senso…
Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication
Haichao Liu, Zhenmin Huang, Zicheng Zhu +3
This paper investigates a cooperative motion planning problem for large-scale connected autonomous vehicles (CAVs) under limited communications, which addresses the challenges of h…
Geometry-Aware Safety-Critical Local Reactive Controller for Robot Navigation in Unknown and Cluttered Environments
Yulin Li, Xindong Tang, Kai Chen +3
This work proposes a safety-critical local reactive controller that enables the robot to navigate in unknown and cluttered environments. In particular, the trajectory tracking task…
Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for Autonomous Driving in Congested Traffic
Lei Zheng, Rui Yang, Zengqi Peng +3
Multi-modal behaviors exhibited by surrounding vehicles (SVs) can typically lead to traffic congestion and reduce the travel efficiency of autonomous vehicles (AVs) in dense traffi…
Integrated Behavior Planning and Motion Control for Autonomous Vehicles with Traffic Rules Compliance
Haichao Liu, Kai Chen, Yulin Li +3
In this article, we propose an optimization-based integrated behavior planning and motion control scheme, which is an interpretable and adaptable urban autonomous driving solution…
Parallel Optimization with Hard Safety Constraints for Cooperative Planning of Connected Autonomous Vehicles
Zhenmin Huang, Haichao Liu, Shaojie Shen +1
The development of connected autonomous vehicles (CAVs) facilitates the enhancement of traffic efficiency in complicated scenarios. In unsignalized roundabout scenarios, difficulti…