1 citations · 1 across the 15 of their papers we have counts for
17 papers
MAIA: Anticipating Interaction Anomalies in Contact-Rich Manipulation via Multimodal World Models
Yi Qiao, Maolin Zheng, Haoran Sun +2
Reliable contact-rich manipulation requires detecting execution deviations early enough for corrective action. Yet most monitoring methods are reactive, identifying anomalies only…
ESCAPE: Egocentric Sensing for Collision Avoidance and Policy Execution
Weiqing Shen, Wenhao Huang, Baokang Zhang +5
Motion planning in unstructured environments is a bottleneck for robot deployment. Current visuomotor policies predominantly rely on external cameras, introducing perceptual blind…
Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Contact-Rich Assembly
Yuhan Wang, Yurou Chen, Hongye Jiang +2
Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained contact. Although simulation-bas…
Triplet2Track: A Hierarchical System with Object-Centric Representations for Reliable Long-Horizon Manipulation
Jianxiang Liu, Gaojing Zhang, Chuan Wen +4
Ensuring reliability in uncertain environments remains difficult for long-horizon robotic manipulation. End-to-end VLA models are data-heavy and opaque, making diagnosis and verifi…
Practice Makes Policies: Bootstrapping and Consolidating Robotic Capabilities from Zero Human Demonstrations
Jialiang Li, Yuhan Wang, Haojun Li +5
General-purpose robotic manipulation requires robots to perform diverse tasks in open-world environments while improving their skills over time. Despite recent progress in robotic…
HALOMI: Learning Humanoid Loco-Manipulation with Active Perception from Human Demonstrations
Zehui Zhao, Yuxuan Zhao, Gaojing Zhang +3
Human demonstrations, which can be collected at scale and naturally capture active hand-eye coordination, are a promising data source for learning humanoid loco-manipulation. Howev…
FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation
Haotian He, Zeyu Yan, Qipeng Liu +2
Force signals provide critical interaction cues for contact-rich robotic manipulation. However, existing methods mostly use force as an additional observation modality, without ful…
BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models
Zhongxi Chen, Yifan Han, Bin Qiu +8
Vision-Language-Action (VLA) policies provide strong behavioral priors for dexterous manipulation, yet adapting them on real robots remains challenging because high-DoF contact fai…
BridgeACT: Bridging Human Demonstrations to Robot Actions via Unified Tool-Target Affordances
Yifan Han, Jianxiang Liu, Haoyu Zhang +3
Learning robot manipulation from human videos is appealing due to the scale and diversity of human demonstrations, but transferring such demonstrations to executable robot behavior…
DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation
Yifan Han, Zhongxi Chen, Yuxuan Zhao +5
While Vision-Language-Action (VLA) models have demonstrated promising generalization capabilities in robotic manipulation, deploying them on specific and complex downstream tasks s…
Viewpoint Matters: Dynamically Optimizing Viewpoints with Masked Autoencoder for Visual Manipulation
Pengfei Yi, Yifan Han, Junyan Li +2
Robotic manipulation continues to be a challenge, and imitation learning (IL) enables robots to learn tasks from expert demonstrations. Current IL methods typically rely on fixed c…
CLASH: Collision Learning via Augmented Sim-to-real Hybridization to Bridge the Reality Gap
Haotian He, Ning Guo, Siqi Shi +2
The sim-to-real gap, particularly in the inaccurate modeling of contact-rich dynamics like collisions, remains a primary obstacle to deploying robot policies trained in simulation.…
Act, Sense, Act: Learning Active Perception from Large-Scale Egocentric Human Data
Jialiang Li, Yi Qiao, Yunhan Guo +2
Achieving generalizable manipulation in unconstrained environments requires the robot to proactively resolve information uncertainty, i.e., the capability of active perception. How…
FSAG: Enhancing Human-to-Dexterous-Hand Finger-Specific Affordance Grounding via Diffusion Models
Yifan Han, Yichuan Peng, Pengfei Yi +5
Dexterous grasp synthesis must jointly satisfy functional intent and physical feasibility, yet existing pipelines often decouple semantic grounding from refinement, yielding unstab…
Look, Zoom, Understand: The Robotic Eyeball for Embodied Perception
Jiashu Yang, Yifan Han, Yucheng Xie +2
In embodied AI, visual perception should be active rather than passive: the system must decide where to look and at what scale to sense to acquire maximally informative data under…
SAGE: Scene Graph-Aware Guidance and Execution for Long-Horizon Manipulation Tasks
Jialiang Li, Wenzheng Wu, Gaojing Zhang +2
Successfully solving long-horizon manipulation tasks remains a fundamental challenge. These tasks involve extended action sequences and complex object interactions, presenting a cr…
FoAM: Foresight-Augmented Multi-Task Imitation Policy for Robotic Manipulation
Litao Liu, Wentao Wang, Yifan Han +5
Multi-task imitation learning (MTIL) has shown significant potential in robotic manipulation by enabling agents to perform various tasks using a single policy. This simplifies the…