57 citations · 60 across the 10 of their papers we have counts for
10 papers
SixthSense: Task-Agnostic Proprioception-Only Whole-Body Wrench Estimation for Humanoids
Xingzhou Chen, Xiayan Xu, Yan Ning +8
Humanoid robots are entering our physical world at scale, yet as oversized toys--good at singing and dancing, but short on force-interaction capabilities for practical tasks. Bridg…
Cooperative-Competitive Team Play of Real-World Craft Robots
Rui Zhao, Xihui Li, Yizheng Zhang +6
Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years. However, the efficient training of collect…
AgentWorld: An Interactive Simulation Platform for Scene Construction and Mobile Robotic Manipulation
Yizheng Zhang, Zhenjun Yu, Jiaxin Lai +2
We introduce AgentWorld, an interactive simulation platform for developing household mobile manipulation capabilities. Our platform combines automated scene construction that encom…
HumanVLA: Towards Vision-Language Directed Object Rearrangement by Physical Humanoid
Xinyu Xu, Yizheng Zhang, Yong-Lu Li +2
Physical Human-Scene Interaction (HSI) plays a crucial role in numerous applications. However, existing HSI techniques are limited to specific object dynamics and privileged inform…
Learning Highly Dynamic Behaviors for Quadrupedal Robots
Chong Zhang, Jiapeng Sheng, Tingguang Li +6
Learning highly dynamic behaviors for robots has been a longstanding challenge. Traditional approaches have demonstrated robust locomotion, but the exhibited behaviors lack diversi…
Learning Terrain-Adaptive Locomotion with Agile Behaviors by Imitating Animals
Tingguang Li, Yizheng Zhang, Chong Zhang +5
In this paper, we present a general learning framework for controlling a quadruped robot that can mimic the behavior of real animals and traverse challenging terrains. Our method c…
Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
Lei Han, Qingxu Zhu, Jiapeng Sheng +16
Knowledge from animals and humans inspires robotic innovations. Numerous efforts have been made to achieve agile locomotion in quadrupedal robots through classical controllers or r…
Relative Policy-Transition Optimization for Fast Policy Transfer
Jiawei Xu, Cheng Zhou, Yizheng Zhang +2
We consider the problem of policy transfer between two Markov Decision Processes (MDPs). We introduce a lemma based on existing theoretical results in reinforcement learning to mea…
Trade-off on Sim2Real Learning: Real-world Learning Faster than Simulations
Jingyi Huang, Yizheng Zhang, Fabio Giardina +1
Deep Reinforcement Learning (DRL) experiments are commonly performed in simulated environments due to the tremendous training sample demands from deep neural networks. In contrast,…
Tactical Reward Shaping: Bypassing Reinforcement Learning with Strategy-Based Goals
Yizheng Zhang, Andre Rosendo
Deep Reinforcement Learning (DRL) has shown its promising capabilities to learn optimal policies directly from trial and error. However, learning can be hindered if the goal of the…