83 citations · 181 across the 19 of their papers we have counts for
19 papers
EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
Ruihan Yang, Qinxi Yu, Yecheng Wu +12
Real robot data collection for imitation learning has led to significant advancements in robotic manipulation. However, the requirement for robot hardware in the process fundamenta…
Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation
Jianglong Ye, Keyi Wang, Chengjing Yuan +6
Generating large-scale demonstrations for dexterous hand manipulation remains challenging, and several approaches have been proposed in recent years to address this. Among them, ge…
WildLMa: Long Horizon Loco-Manipulation in the Wild
Ri-Zhao Qiu, Yuchen Song, Xuanbin Peng +8
'In-the-wild' mobile manipulation aims to deploy robots in diverse real-world environments, which requires the robot to (1) have skills that generalize across object configurations…
Visual Manipulation with Legs
Xialin He, Chengjing Yuan, Wenxuan Zhou +3
Animals use limbs for both locomotion and manipulation. We aim to equip quadruped robots with similar versatility. This work introduces a system that enables quadruped robots to in…
ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation
Shiqi Yang, Minghuan Liu, Yuzhe Qin +6
Learning from demonstrations has shown to be an effective approach to robotic manipulation, especially with the recently collected large-scale robot data with teleoperation systems…
Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning
Runyu Ding, Yuzhe Qin, Jiyue Zhu +5
Teleoperation is a crucial tool for collecting human demonstrations, but controlling robots with bimanual dexterous hands remains a challenge. Existing teleoperation systems strugg…
SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models
An-Chieh Cheng, Hongxu Yin, Yang Fu +5
Vision Language Models (VLMs) have demonstrated remarkable performance in 2D vision and language tasks. However, their ability to reason about spatial arrangements remains limited.…
Expressive Whole-Body Control for Humanoid Robots
Xuxin Cheng, Yandong Ji, Junming Chen +3
Can we enable humanoid robots to generate rich, diverse, and expressive motions in the real world? We propose to learn a whole-body control policy on a human-sized robot to mimic h…
Visual Whole-Body Control for Legged Loco-Manipulation
Minghuan Liu, Zixuan Chen, Xuxin Cheng +4
We study the problem of mobile manipulation using legged robots equipped with an arm, namely legged loco-manipulation. The robot legs, while usually utilized for mobility, offer an…
Learning Generalizable Feature Fields for Mobile Manipulation
Ri-Zhao Qiu, Yafei Hu, Yuchen Song +8
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner so that robots can use both for navigation and manipulation. The latter requires c…
Harmonic Mobile Manipulation
Ruihan Yang, Yejin Kim, Rose Hendrix +3
Recent advancements in robotics have enabled robots to navigate complex scenes or manipulate diverse objects independently. However, robots are still impotent in many household tas…
Generalized Animal Imitator: Agile Locomotion with Versatile Motion Prior
Ruihan Yang, Zhuoqun Chen, Jianhan Ma +4
The agility of animals, particularly in complex activities such as running, turning, jumping, and backflipping, stands as an exemplar for robotic system design. Transferring this s…
Neural Volumetric Memory for Visual Locomotion Control
Ruihan Yang, Ge Yang, Xiaolong Wang
Legged robots have the potential to expand the reach of autonomy beyond paved roads. In this work, we consider the difficult problem of locomotion on challenging terrains using a s…
Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization
Chieko Sarah Imai, Minghao Zhang, Yuchen Zhang +4
Developing robust vision-guided controllers for quadrupedal robots in complex environments, with various obstacles, dynamical surroundings and uneven terrains, is very challenging.…
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu +4
While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipula…
Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers
Ruihan Yang, Minghao Zhang, Nicklas Hansen +2
We propose to address quadrupedal locomotion tasks using Reinforcement Learning (RL) with a Transformer-based model that learns to combine proprioceptive information and high-dimen…
Suphx: Mastering Mahjong with Deep Reinforcement Learning
Junjie Li, Sotetsu Koyamada, Qiwei Ye +7
Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI. In recent years, studies on game AI h…
Multi-Task Reinforcement Learning with Soft Modularization
Ruihan Yang, Huazhe Xu, Yi Wu +1
Multi-task learning is a very challenging problem in reinforcement learning. While training multiple tasks jointly allow the policies to share parameters across different tasks, th…
Artificial Intelligence for Prosthetics - challenge solutions
Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty +47
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a giv…