2 citations · 2 across the 4 of their papers we have counts for
7 papers
From Vision to Harvest: Benchmarking Vision-Language Models for Multi-Arm Robotic Fruit Harvesting
Vrishan Inukollu, Adyan Zaman, Anvi Kudaraya +4
Multi-arm robotic harvesting offers a promising path to improve harvesting efficiency and reduce reliance on manual labor. However, practical deployment remains challenging because…
Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?
Varun Kaushik, Yayun Tan, Xiaofan Yu
Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to…
Latent-Action-Guided Video-Language Feature Learning for Surgical Instrument-Tissue Interaction Recognition
Jiajun Cheng, Subarna Tripathi, Sainan Liu +2
Recognizing instrument--tissue interactions is essential for context-aware surgical AI. Vision-language models offer a natural way to inject semantic structure into surgical repres…
TrajPred: Trajectory-Conditioned Joint Embedding Prediction for Surgical Instrument-Tissue Interaction Recognition in Vision-Language Models
Jiajun Cheng, Xiaofan Yu, Subarna Tripathi +2
Recognizing instruments' interactions with tissues is essential for building context-aware AI assistants in robotic surgery. Vision-language models (VLMs) have opened a new avenue…
SurgXBench: Explainable Vision-Language Model Benchmark for Surgery
Jiajun Cheng, Xianwu Zhao, Sainan Liu +5
Innovations in digital intelligence are transforming robotic surgery with more informed decision-making. Real-time awareness of surgical instrument presence and actions (e.g., cutt…
SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge
Xiaofan Yu, Yunhui Guo, Sicun Gao +1
Unsupervised lifelong learning refers to the ability to learn over time while memorizing previous patterns without supervision. Although great progress has been made in this direct…
A Robotic Auto-Focus System based on Deep Reinforcement Learning
Xiaofan Yu, Runze Yu, Jingsong Yang +1
Considering its advantages in dealing with high-dimensional visual input and learning control policies in discrete domain, Deep Q Network (DQN) could be an alternative method of tr…