7 citations · 15 across the 12 of their papers we have counts for
9 papers · 1 filter
FeelWorld: Visuo-Tactile World Model for Hierarchical Contact Prediction and Planning
Wenxuan Ma, Chaofan Zhang, Chao Xue +4
Humans plan physical interactions by imagining the possible outcomes of candidate actions. However, existing visual world models primarily capture appearance dynamics while overloo…
CoRe: Combined Rewards with Vision-Language Model Feedback for Preference-Aligned Reinforcement Learning
Hexian Ni, Tao Lu, Yinghao Cai
Reward design remains a central challenge in reinforcement learning (RL). Hand-crafted rewards are often difficult to specify and may lead to suboptimal policies, while learned rew…
FG-CLTP: Fine-Grained Contrastive Language Tactile Pretraining for Robotic Manipulation
Wenxuan Ma, Chaofan Zhang, Yinghao Cai +3
Recent advancements in integrating tactile sensing into vision-language-action (VLA) models have demonstrated transformative potential for robotic perception. However, existing tac…
MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping
Qingyu Fan, Yinghao Cai, Chao Li +5
Robotic grasping faces challenges in adapting to objects with varying shapes and sizes. In this paper, we introduce MISCGrasp, a volumetric grasping method that integrates multi-sc…
SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Hexian Ni, Tao Lu, Haoyuan Hu +2
Preference-based Reinforcement Learning (PbRL) methods provide a solution to avoid reward engineering by learning reward models based on human preferences. However, poor feedback-…
CLTP: Contrastive Language-Tactile Pre-training for 3D Contact Geometry Understanding
Wenxuan Ma, Xiaoge Cao, Yixiang Zhang +7
Recent advancements in integrating tactile sensing with vision-language models (VLMs) have demonstrated remarkable potential for robotic multimodal perception. However, existing ta…