2 citations · 9 across the 27 of their papers we have counts for
26 papers · 1 filter
T-WLA: Bridging Slow Vision and Fast Touch
Kaichen Zhou, Yuzhen Chen, Hannah Yao +14
In contact-rich manipulation, contact conditions can change before a visual policy updates its action plan. Recomputing the plan through the visual policy adds latency, while tacti…
ACTS: Learning Visuo-Tactile World Models from Human Physical Interaction
Yuxiang Ma, Weihan Xu, Pengfei Ye +5
Abstract Action-conditioned world models for contact-rich manipulation require data that captures how contact forms, slides, and releases under varying force. Such data are slow to…
Robot Learning with Visual Predicted Force
Haonan Chen, Feiyang Wu, Yuxiang Ma +11
Force-aware manipulation typically relies on specialized force or tactile sensors. We show that force-aware manipulation can instead be achieved through visual force prediction fro…
GlowTact: Simple and Compact Vision-Based Tactile Sensing with High Sensitivity and Spatial Resolution
Yuxiang Ma, Megha Tippur, Pengfei Ye +4
Vision-based tactile sensors (VBTS) provide rich contact information for robotic manipulation, but existing designs can be hard to simplify and adapt to the size and constraints of…
A Brain-inspired Hierarchical Framework for Zero-Shot Robot Task Reasoning and Execution
Guangming Wang, Pengfei Ye, Qizhen Ying +7
Robots that follow open-ended language instructions need to connect semantic intent to visual scene understanding, geometric feasibility, object states, and physical interaction co…
ARGUS: Aligning Robot Scene Geometry Under Shifting Views with Large 3D Vision Models
Rishik Sathua, Haonan Chen, Katherine Driggs-Campbell
Large-scale visuomotor policies have demonstrated impressive performance across a wide range of robot manipulation tasks. However, despite this success, manipulation polices often…
DriftWorld: Fast World Modeling through Drifting
Susie Lu, Haonan Chen, Weirui Ye +1
Predictive world models enable robots to simulate the visual outcomes of their actions, but state-of-the-art diffusion-based models remain costly because generating each rollout re…
Ordered Action Tokens for Visuomotor Policy Learning
Chaoqi Liu, Yue Zhao, Haonan Chen +4
Action tokenization maps continuous robot action chunks to discrete tokens and has become an important interface for modern visuomotor policies. Existing approaches either rely on…
B-spline Policy: Accelerating Manipulation Policies via B-spline Action Representations
Xiaoshen Han, Haoyu Xiong, Haonan Chen +4
In this work, we present B-spline Policy (BSP), an action representation designed for accelerating robot manipulation policies. Rather than predicting discrete-time action chunks,…
You Only Touch Once: 6-DoF Object Pose Estimation from Single Tactile Contact
Pengfei Ye, Yuxiang Ma, Haonan Chen +5
Accurate 6-DoF object pose estimation is fundamental to robotic manipulation, yet vision-based methods often fail under occlusion, poor lighting, and reflective or transparent surf…
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.…
IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation
Jiawei Gao, Chaoqi Liu, Peilin Wu +2
Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting objects with different masses,…
RoTri-Diff: A Spatial Robot-Object Triadic Interaction-Guided Diffusion Model for Bimanual Manipulation
Zixuan Chen, Nga Teng Chan, Yiwen Hou +8
Bimanual manipulation is a fundamental robotic skill that requires continuous and precise coordination between two arms. While imitation learning (IL) is the dominant paradigm for…
OAT: Ordered Action Tokenization
Chaoqi Liu, Xiaoshen Han, Jiawei Gao +3
Autoregressive policies offer a compelling foundation for scalable robot learning by enabling discrete abstraction, token-level reasoning, and flexible inference. However, applying…
Flexible Multitask Learning with Factorized Diffusion Policy
Chaoqi Liu, Haonan Chen, Sigmund H. Høeg +4
Multitask learning poses significant challenges due to the highly multimodal and diverse nature of robot action distributions. However, effectively fitting policies to these comple…
SIMPACT: Simulation-Enabled Action Planning using Vision-Language Models
Haowen Liu, Shaoxiong Yao, Haonan Chen +4
Vision-Language Models (VLMs) exhibit remarkable common-sense and semantic reasoning capabilities. However, they lack a grounded understanding of physical dynamics. This limitation…
AdaptPNP: Integrating Prehensile and Non-Prehensile Skills for Adaptive Robotic Manipulation
Jinxuan Zhu, Chenrui Tie, Xinyi Cao +8
Non-prehensile (NP) manipulation, in which robots alter object states without forming stable grasps (for example, pushing, poking, or sliding), significantly broadens robotic manip…
Manual2Skill++: Connector-Aware General Robotic Assembly from Instruction Manuals via Vision-Language Models
Chenrui Tie, Shengxiang Sun, Yudi Lin +9
Assembly hinges on reliably forming connections between parts; yet most robotic approaches plan assembly sequences and part poses while treating connectors as an afterthought. Conn…
Goal-VLA: Image-Generative VLMs as Object-Centric World Models Empowering Zero-shot Robot Manipulation
Haonan Chen, Jingxiang Guo, Bangjun Wang +7
Generalization remains a fundamental challenge in robotic manipulation. To tackle this challenge, recent Vision-Language-Action (VLA) models build policies on top of Vision-Languag…
Tool-as-Interface: Learning Robot Policies from Observing Human Tool Use
Haonan Chen, Cheng Zhu, Shuijing Liu +2
Tool use is essential for enabling robots to perform complex real-world tasks, but learning such skills requires extensive datasets. While teleoperation is widely used, it is slow,…
Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models
Haonan Chen, Jiaming Xu, Lily Sheng +4
When performing tasks like laundry, humans naturally coordinate both hands to manipulate objects and anticipate how their actions will change the state of the clothes. However, ach…
Towards Uncertainty Unification: A Case Study for Preference Learning
Shaoting Peng, Haonan Chen, Katherine Driggs-Campbell
Learning human preferences is essential for human-robot interaction, as it enables robots to adapt their behaviors to align with human expectations and goals. However, the inherent…
MetaFold: Language-Guided Multi-Category Garment Folding Framework via Trajectory Generation and Foundation Model
Haonan Chen, Junxiao Li, Ruihai Wu +9
Garment folding is a common yet challenging task in robotic manipulation. The deformability of garments leads to a vast state space and complex dynamics, which complicates precise…
Manual2Skill: Learning to Read Manuals and Acquire Robotic Skills for Furniture Assembly Using Vision-Language Models
Chenrui Tie, Shengxiang Sun, Jinxuan Zhu +7
Humans possess an extraordinary ability to understand and execute complex manipulation tasks by interpreting abstract instruction manuals. For robots, however, this capability rema…