31 citations · 74 across the 7 of their papers we have counts for
7 papers
Hierarchical control and learning of a foraging CyberOctopus
Chia-Hsien Shih, Noel Naughton, Udit Halder +5
Inspired by the unique neurophysiology of the octopus, we propose a hierarchical framework that simplifies the coordination of multiple soft arms by decomposing control into high-l…
Robotic Interestingness via Human-Informed Few-Shot Object Detection
Seungchan Kim, Chen Wang, Bowen Li +1
Interestingness recognition is crucial for decision making in autonomous exploration for mobile robots. Previous methods proposed an unsupervised online learning approach that can…
Disentangling Successor Features for Coordination in Multi-agent Reinforcement Learning
Seung Hyun Kim, Neale Van Stralen, Girish Chowdhary +1
Multi-agent reinforcement learning (MARL) is a promising framework for solving complex tasks with many agents. However, a key challenge in MARL is defining private utility function…
AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
Bowen Li, Chen Wang, Pranay Reddy +2
Few-shot object detection has attracted increasing attention and rapidly progressed in recent years. However, the requirement of an exhaustive offline fine-tuning stage in existing…
Unsupervised Online Learning for Robotic Interestingness with Visual Memory
Chen Wang, Yuheng Qiu, Wenshan Wang +3
Autonomous robots frequently need to detect "interesting" scenes to decide on further exploration, or to decide which data to share for cooperation. These scenarios often require f…
Semi-supervised Learning for COVID-19 Image Classification via ResNet
Lucy Nwosu, Xiangfang Li, Lijun Qian +2
Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the internationa…
Combating the Compounding-Error Problem with a Multi-step Model
Kavosh Asadi, Dipendra Misra, Seungchan Kim +1
Model-based reinforcement learning is an appealing framework for creating agents that learn, plan, and act in sequential environments. Model-based algorithms typically involve lear…