1 citations · 1 across the 13 of their papers we have counts for
14 papers
RoboIRS: Inference-Time Internal Representation Steering for Generalist Robot Policies
Jiuzhou Lei, Chang Liu, Dayou Li +5
Vision-language-action (VLA) and world-action models (WAMs) often degrade under out-of-distribution task variations despite retaining partial task capability. To recover such capab…
PHR-VLA: Planning Horizon Reasoning for Vision-Language-Action Models
Davood Soleymanzadeh, Kaidi Zhang, Zhiyuan Zhang +4
Vision-language-action models (VLAs) have shown strong promise for general-purpose robotic manipulation by mapping language instructions and vision observations directly to actions…
TAC-LOCO: Unified Whole-Body Control for Quadrupedal TACtile-Informed LOCO-Manipulation
Muqun Hu, Yuhao Zhou, Kabir Ray Malik +4
Dynamic loco-manipulation requires legged robots to coordinate whole-body motion while maintaining stable physical interaction with grasped objects under uncertain external forces.…
Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations
Zhiyuan Zhang, Adeesh Desai, Jyun-Chi Hu +7
Tactile sensing can substantially improve contact-rich robotic manipulation, yet its practical deployment remains limited by the fragility, calibration requirements, and maintenanc…
ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation
Zhiyuan Zhang, Pokuang Zhou, Kaidi Zhang +7
Contact-rich manipulation poses a fundamental challenge for world models: visual and tactile observations capture different aspects of physical interaction, and their utility depen…
Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
Pokuang Zhou, Yuhao Zhou, Quan Khanh Luu +7
Quadrupedal loco-manipulation is commonly built on visual perception and proprioception. Yet reliable contact-rich manipulation remains difficult: vision and proprioception alone c…
Learning When to See and When to Feel: Adaptive Vision-Torque Fusion for Contact-Aware Manipulation
Jiuzhou Lei, Chang Liu, Yu She +2
Vision-based policies have achieved a good performance in robotic manipulation due to the accessibility and richness of visual observations. However, purely visual sensing becomes…
StemVLA:An Open-Source Vision-Language-Action Model with Future 3D Spatial Geometry Knowledge and 4D Historical Representation
Jiasong Xiao, Yutao She, Kai Li +2
Vision-language-action (VLA) models integrate visual observations and language instructions to predict robot actions, demonstrating promising generalization in manipulation tasks.…
Canonical Policy: Learning Canonical 3D Representation for SE(3)-Equivariant Policy
Zhiyuan Zhang, Zhengtong Xu, Jai Nanda Lakamsani +1
Visual Imitation learning has achieved remarkable progress in robotic manipulation, yet generalization to unseen objects, scene layouts, and camera viewpoints remains a key challen…
ManiFeel: Benchmarking and Understanding Visuotactile Manipulation Policy Learning
Quan Khanh Luu, Pokuang Zhou, Zhengtong Xu +3
Supervised visuomotor policies have shown strong performance in robotic manipulation but often struggle in tasks with limited visual inputs, such as operations in confined spaces a…
DiffOG: Differentiable Policy Trajectory Optimization with Generalizability
Zhengtong Xu, Zichen Miao, Qiang Qiu +2
Imitation learning-based visuomotor policies excel at manipulation tasks but often produce suboptimal action trajectories compared to model-based methods. Directly mapping camera d…
VILP: Imitation Learning with Latent Video Planning
Zhengtong Xu, Qiang Qiu, Yu She
In the era of generative AI, integrating video generation models into robotics opens new possibilities for the general-purpose robot agent. This paper introduces imitation learning…
LeTac-MPC: Learning Model Predictive Control for Tactile-reactive Grasping
Zhengtong Xu, Yu She
Grasping is a crucial task in robotics, necessitating tactile feedback and reactive grasping adjustments for robust grasping of objects under various conditions and with differing…
LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory Optimization
Zhengtong Xu, Yu She
This paper introduces LeTO, a method for learning constrained visuomotor policy with differentiable trajectory optimization. Our approach integrates a differentiable optimization l…