7 citations · 16 across the 6 of their papers we have counts for
3 papers · 1 filter
MARVL: Multi-Stage Guidance for Robotic Manipulation via Vision-Language Models
Xunlan Zhou, Xuanlin Chen, Shaowei Zhang +4
Designing dense reward functions is pivotal for efficient robotic Reinforcement Learning (RL). However, most dense rewards rely on manual engineering, which fundamentally limits th…
Learned Slip-Detection-Severity Framework using Tactile Deformation Field Feedback for Robotic Manipulation
Neel Jawale, Navneet Kaur, Amy Santoso +2
Safely handling objects and avoiding slippage are fundamental challenges in robotic manipulation, yet traditional techniques often oversimplify the issue by treating slippage as a…
Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy
Xiaohai Hu, Aparajit Venkatesh, Yusen Wan +5
Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for gr…