317 citations · 397 across the 11 of their papers we have counts for
12 papers
DiffMimic: Efficient Motion Mimicking with Differentiable Physics
Jiawei Ren, Cunjun Yu, Siwei Chen +3
Motion mimicking is a foundational task in physics-based character animation. However, most existing motion mimicking methods are built upon reinforcement learning (RL) and suffer…
Hi Sheldon! Creating Deep Personalized Characters from TV Shows
Meidai Xuanyuan, Yuwang Wang, Honglei Guo +4
Imagine an interesting multimodal interactive scenario that you can see, hear, and chat with an AI-generated digital character, who is capable of behaving like Sheldon from The Big…
DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics
Siwei Chen, Yiqing Xu, Cunjun Yu +4
Deformable Object Manipulation (DOM) is of significant importance to both daily and industrial applications. Recent successes in differentiable physics simulators allow learning al…
Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects
Xiao Ma, David Hsu, Wee Sun Lee
Manipulating deformable objects, such as ropes and clothing, is a long-standing challenge in robotics, because of their large degrees of freedom, complex non-linear dynamics, and s…
Towards Overcoming False Positives in Visual Relationship Detection
Daisheng Jin, Xiao Ma, Chongzhi Zhang +8
In this paper, we investigate the cause of the high false positive rate in Visual Relationship Detection (VRD). We observe that during training, the relationship proposal distribut…
Contrastive Variational Reinforcement Learning for Complex Observations
Xiao Ma, Siwei Chen, David Hsu +1
Deep reinforcement learning (DRL) has achieved significant success in various robot tasks: manipulation, navigation, etc. However, complex visual observations in natural environmen…
DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space
Siwei Chen, Xiao Ma, David Hsu
It has been arduous to assess the progress of a policy learning algorithm in the domain of hierarchical task with high dimensional action space due to the lack of a commonly accept…
Balanced Meta-Softmax for Long-Tailed Visual Recognition
Jiawei Ren, Cunjun Yu, Shunan Sheng +4
Deep classifiers have achieved great success in visual recognition. However, real-world data is long-tailed by nature, leading to the mismatch between training and testing distribu…
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
Cunjun Yu, Xiao Ma, Jiawei Ren +2
Understanding crowd motion dynamics is critical to real-world applications, e.g., surveillance systems and autonomous driving. This is challenging because it requires effectively m…
Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations
Xiao Ma, Peter Karkus, David Hsu +2
Deep reinforcement learning is successful in decision making for sophisticated games, such as Atari, Go, etc. However, real-world decision making often requires reasoning with part…
Differentiable Algorithm Networks for Composable Robot Learning
Peter Karkus, Xiao Ma, David Hsu +3
This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a…
Particle Filter Recurrent Neural Networks
Xiao Ma, Peter Karkus, David Hsu +1
Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data. To tackle highly variable and noisy real-world data, we introduce Particl…