51 citations · 87 across the 10 of their papers we have counts for
12 papers
Learning Physical Dynamics with Subequivariant Graph Neural Networks
Jiaqi Han, Wenbing Huang, Hengbo Ma +3
Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics. However, they still encounter several challenges: 1) Physical laws abide by symmetry, whi…
Important Object Identification with Semi-Supervised Learning for Autonomous Driving
Jiachen Li, Haiming Gang, Hengbo Ma +2
Accurate identification of important objects in the scene is a prerequisite for safe and high-quality decision making and motion planning of intelligent agents (e.g., autonomous ve…
Cross Domain Robot Imitation with Invariant Representation
Zhao-Heng Yin, Lingfeng Sun, Hengbo Ma +2
Animals are able to imitate each others' behavior, despite their difference in biomechanics. In contrast, imitating the other similar robots is a much more challenging task in robo…
RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
Jiachen Li, Fan Yang, Hengbo Ma +3
Motion forecasting plays a significant role in various domains (e.g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of his…
Spectral Temporal Graph Neural Network for Trajectory Prediction
Defu Cao, Jiachen Li, Hengbo Ma +1
An effective understanding of the contextual environment and accurate motion forecasting of surrounding agents is crucial for the development of autonomous vehicles and social mobi…
Speed Planning Using Bezier Polynomials with Trapezoidal Corridors
Jialun Li, Xiaojia Xie, Hengbo Ma +2
To generate safe and real-time trajectories for an autonomous vehicle in dynamic environments, path and speed decoupled planning methods are often considered. This paper studies sp…