activity
20192026
most citedTactical Reward Shaping: Bypassing Reinforcement Learning with Strategy-Based Goals

1 citations · 1 across the 6 of their papers we have counts for

collaborators

8 papers

cs.RO2026

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…

cs.RO2026

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…

cs.RO2025

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…

cs.RO2024

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…

cs.RO2024

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…

cs.RO2023

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…