103 citations · 138 across the 14 of their papers we have counts for
14 papers
Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors
Lingfeng Sun, Yixiao Wang, Pin-Yun Hung +4
Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance a…
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Zhuo Xu, Rui Zhou, Yida Yin +3
Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications. However, collecting massive…
Distributed Multi-agent Interaction Generation with Imagined Potential Games
Lingfeng Sun, Pin-Yun Hung, Changhao Wang +2
Interactive behavior modeling of multiple agents is an essential challenge in simulation, especially in scenarios when agents need to avoid collisions and cooperate at the same tim…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Online Learning Based Mobile Robot Controller Adaptation for Slip Reduction
Huidong Gao, Rui Zhou, Masayoshi Tomizuka +1
Slip is a very common phenomena present in wheeled mobile robotic systems. It has undesirable consequences such as wasting energy and impeding system stability. To tackle the chall…
Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting
Rui Zhou, Hongyu Zhou, Huidong Gao +3
Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge. Recent advances in using data-driven approaches ha…
History Encoding Representation Design for Human Intention Inference
Zhuo Xu, Masayoshi Tomizuka
In this extended abstract, we investigate the design of learning representation for human intention inference. In our designed human intention prediction task, we propose a history…
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing
Zhuo Xu, Wenhao Yu, Alexander Herzog +6
General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics. Deep reinforcement learning…
RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer
Daniel Ho, Kanishka Rao, Zhuo Xu +3
The success of deep reinforcement learning (RL) and imitation learning (IL) in vision-based robotic manipulation typically hinges on the expense of large scale data collection. Wit…
Guided Policy Search Model-based Reinforcement Learning for Urban Autonomous Driving
Zhuo Xu, Jianyu Chen, Masayoshi Tomizuka
In this paper, we continue our prior work on using imitation learning (IL) and model free reinforcement learning (RL) to learn driving policies for autonomous driving in urban scen…
Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networks
Haonan Chang, Zhuo Xu, Masayoshi Tomizuka
Reinforcement learning methods have been developed to achieve great success in training control policies in various automation tasks. However, a main challenge of the wider applica…
End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning
Jianyu Chen, Zhuo Xu, Masayoshi Tomizuka
Current autonomous driving systems are composed of a perception system and a decision system. Both of them are divided into multiple subsystems built up with lots of human heuristi…
Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control
Zhuo Xu, Chen Tang, Masayoshi Tomizuka
Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving h…
Cascade Attribute Learning Network
Zhuo Xu, Haonan Chang, Masayoshi Tomizuka
We propose the cascade attribute learning network (CALNet), which can learn attributes in a control task separately and assemble them together. Our contribution is twofold: first w…