26 citations · 62 across the 25 of their papers we have counts for
11 papers · 2 filters
Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World
Laura Smith, J. Chase Kew, Xue Bin Peng +3
Legged robots are physically capable of traversing a wide range of challenging environments, but designing controllers that are sufficiently robust to handle this diversity has bee…
Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning
Hao-Lun Hsu, Qiuhua Huang, Sehoon Ha
One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases. In this work, we propose a novel technique of unsupervis…
Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning
K. Niranjan Kumar, Irfan Essa, Sehoon Ha
We introduce a novel method to teach a robotic agent to interactively explore cluttered yet structured scenes, such as kitchen pantries and grocery shelves, by leveraging the physi…
Learning Robot Structure and Motion Embeddings using Graph Neural Networks
J. Taery Kim, Jeongeun Park, Sungjoon Choi +1
We propose a learning framework to find the representation of a robot's kinematic structure and motion embedding spaces using graph neural networks (GNN). Finding a compact and low…
Learning to Navigate Sidewalks in Outdoor Environments
Maks Sorokin, Jie Tan, C. Karen Liu +1
Outdoor navigation on sidewalks in urban environments is the key technology behind important human assistive applications, such as last-mile delivery or neighborhood patrol. This p…
FastMimic: Model-based Motion Imitation for Agile, Diverse and Generalizable Quadrupedal Locomotion
Tianyu Li, Jungdam Won, Sehoon Ha +1
Robots operating in human environments need various skills, like slow and fast walking, turning, side-stepping, and many more. However, building robot controllers that can exhibit…