2k citations · 2.2k across the 36 of their papers we have counts for
11 papers · 1 filter
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…
Solving Challenging Control Problems Using Two-Staged Deep Reinforcement Learning
Nitish Sontakke, Sehoon Ha
We present a deep reinforcement learning (deep RL) algorithm that consists of learning-based motion planning and imitation to tackle challenging control problems. Deep RL has been…
PM-FSM: Policies Modulating Finite State Machine for Robust Quadrupedal Locomotion
Ren Liu, Nitish Sontakke, Sehoon Ha
Deep reinforcement learning (deep RL) has emerged as an effective tool for developing controllers for legged robots. However, vanilla deep RL often requires a tremendous amount of…
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge
Naoki Yokoyama, Qian Luo, Dhruv Batra +1
Recent advances in deep reinforcement learning and scalable photorealistic simulation have led to increasingly mature embodied AI for various visual tasks, including navigation. Ho…
Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments
Visak Kumar, Sehoon Ha, C. Karen Liu
Simulation provides a safe and efficient way to generate useful data for learning complex robotic tasks. However, matching simulation and real-world dynamics can be quite challengi…
Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation
Naoki Yokoyama, Sehoon Ha, Dhruv Batra
We present Success weighted by Completion Time (SCT), a new metric for evaluating navigation performance for mobile robots. Several related works on navigation have used Success we…