3 citations · 8 across the 17 of their papers we have counts for
17 papers
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Yunhai Feng, Natalie Leung, Jiaxuan Wang +3
Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learni…
ForceBand: Learning Forceful Manipulation with sEMG
Botao He, Zhi Wang, Linna Kuang +8
Human demonstrations are a scalable data source for learning robot manipulation policies. However, common sources of human demonstration data, such as motion-capture trajectories a…
HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies
Amber Xie, Haozhi Qi, Dorsa Sadigh
Mastering dexterous manipulation with multi-fingered hands has been a grand challenge in robotics for decades. Despite its potential, the difficulty of collecting high-quality data…
Coordinated Humanoid Manipulation with Choice Policies
Haozhi Qi, Yen-Jen Wang, Toru Lin +4
Humanoid robots hold great promise for operating in human-centric environments, yet achieving robust whole-body coordination across the head, hands, and legs remains a major challe…
OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer
Jessica Yin, Haozhi Qi, Youngsun Wi +7
Human video demonstrations provide abundant training data for learning robot policies, but video alone cannot capture the rich contact signals critical for mastering manipulation.…
Learning Dexterous Manipulation Skills from Imperfect Simulations
Elvis Hsieh, Wen-Han Hsieh, Yen-Jen Wang +4
Reinforcement learning and sim-to-real transfer have made significant progress in dexterous manipulation. However, progress remains limited by the difficulty of simulating complex…
Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations
Irmak Guzey, Haozhi Qi, Julen Urain +10
Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community. Achieving this would mark si…
SPIDER: Scalable Physics-Informed Dexterous Retargeting
Chaoyi Pan, Changhao Wang, Haozhi Qi +8
Learning agile robotic policies requires large-scale demonstrations, but translating abundant human motion data to robots is bottlenecked by the embodiment gap and missing dynamic…
From Simple to Complex Skills: The Case of In-Hand Object Reorientation
Haozhi Qi, Brent Yi, Mike Lambeta +3
Learning policies in simulation and transferring them to the real world has become a promising approach in dexterous manipulation. However, bridging the sim-to-real gap for each ne…
Digitizing Touch with an Artificial Multimodal Fingertip
Mike Lambeta, Tingfan Wu, Ali Sengul +20
Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using…
Hand-Object Interaction Pretraining from Videos
Himanshu Gaurav Singh, Antonio Loquercio, Carmelo Sferrazza +4
We present an approach to learn general robot manipulation priors from 3D hand-object interaction trajectories. We build a framework to use in-the-wild videos to generate sensorimo…
Lessons from Learning to Spin "Pens"
Jun Wang, Ying Yuan, Haichuan Che +4
In-hand manipulation of pen-like objects is an important skill in our daily lives, as many tools such as hammers and screwdrivers are similarly shaped. However, current learning-ba…
Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing
Jessica Yin, Haozhi Qi, Jitendra Malik +3
Recent progress in reinforcement learning (RL) and tactile sensing has significantly advanced dexterous manipulation. However, these methods often utilize simplified tactile signal…
Learning Visuotactile Skills with Two Multifingered Hands
Toru Lin, Yu Zhang, Qiyang Li +4
Aiming to replicate human-like dexterity, perceptual experiences, and motion patterns, we explore learning from human demonstrations using a bimanual system with multifingered hand…
Twisting Lids Off with Two Hands
Toru Lin, Zhao-Heng Yin, Haozhi Qi +2
Manipulating objects with two multi-fingered hands has been a long-standing challenge in robotics, due to the contact-rich nature of many manipulation tasks and the complexity inhe…
Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation
Sudharshan Suresh, Haozhi Qi, Tingfan Wu +9
To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, suc…
Perceiving Extrinsic Contacts from Touch Improves Learning Insertion Policies
Carolina Higuera, Joseph Ortiz, Haozhi Qi +3
Robotic manipulation tasks such as object insertion typically involve interactions between object and environment, namely extrinsic contacts. Prior work on Neural Contact Fields (N…