Search Results for author:
Found 6 papers, 1 papers with code
Contact Energy Based Hindsight Experience Prioritization
Multi-goal robot manipulation tasks with sparse rewards are difficult for reinforcement learning (RL) algorithms due to the inefficiency in collecting successful experiences.
FViT-Grasp: Grasping Objects With Using Fast Vision Transformers
This study addresses the challenge of manipulation, a prominent issue in robotics.
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand.
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning
Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process.
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks
In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration.
Visualization of Nonlinear Programming for Robot Motion Planning
Nonlinear programming targets nonlinear optimization with constraints, which is a generic yet complex methodology involving humans for problem modeling and algorithms for problem solving.
Motion Planning
Robotics
Human-Computer Interaction
Numerical Analysis
Numerical Analysis
H.5.2; G.1.6
