112 citations · 304 across the 149 of their papers we have counts for
12 papers · 2 filters
FACT: A Multimodal Force-Action-Track Foundation Model for Human-to-Robot Dexterity
Zhi Cao, Yining Hong, Jiaolong Yang +4
Human demonstrations offer a scalable alternative to costly robot data collection for dexterous manipulation, but most human datasets lack force measurements. Dexterous manipulatio…
I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation
Wensi Ai, Christopher Luey, Karthik Dharmarajan +7
Policies trained offline from teleoperated demonstrations can degrade outside their training distribution. This gap is particularly acute in long-horizon whole-body mobile manipula…
The Human--VLA Communication Gap: What Humans Leave Implicit and What Policies Cannot Recover
Yiqing Xu, Jiajun Wu
A robot may know how to perform a task yet fail when a human asks for it naturally. We study this human--VLA communication gap by separating an \emph{information gap}, where task s…
One Demonstration, Many Objects: Generalizing Manipulation via Local Contact Geometry
Satvik Sharma, Samrat Sahoo, Huang Huang +4
Dexterous manipulation with multi-fingered robot hands promises human-level dexterity, but collecting large-scale dexterous robot hand data remains difficult. Learning from human d…
TrAct: Bridging Robot Control and Visual Prediction with Visual Tracks
Zhi Cao, Howard Ji, Kevin Zhang +4
Robot actions are inherently embodiment-specific and only weakly aligned with image-space visual changes, limiting their effectiveness as conditioning signals for robot world model…
CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation
Haonan Chen, Yuxiang Ma, Stephen Tian +7
Long-horizon, contact-rich complex manipulation tasks, such as seating a GPU into a PCIe slot, demand both millimeter high precision and out-of-the-box generalization to new tasks.…
InSight: Self-Guided Skill Acquisition via Steerable VLAs
Maggie Wang, Lars Osterberg, Stephen Tian +5
Vision-language-action (VLA) models excel at robot manipulation via imitation learning, but adapting them to new tasks often requires additional human demonstrations, which can be…
DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
Jadelynn Dao, Milan Ganai, Yasmina Abukhadra +7
Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time compute to improve capability. Ho…
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception
Evans Han, Yunfan Jiang, Yingke Wang +6
Recent advances in robot imitation learning have produced powerful visuomotor policies that manipulate diverse objects from visual inputs. However, monocular observations lack dept…
World Model for Robot Learning: A Comprehensive Survey
Bohan Hou, Gen Li, Jindou Jia +15
World models, which are predictive representations of how environments evolve under actions, have become a central component of robot learning. They support policy learning, planni…
IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction
Yingke Wang, Hao Li, Yifeng Zhu +6
Robotic reproduction of oil paintings using soft brushes and pigments requires force-sensitive control of deformable tools, prediction of brushstroke effects, and multi-step stroke…
CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
Letian Fu, Justin Yu, Karim El-Refai +13
"Code-as-Policy" considers how executable code can complement data-intensive Vision-Language-Action (VLA) methods, yet their effectiveness as autonomous controllers for embodied ma…