Towards advanced robotic manipulation

09/19/2022
by   Francisco Roldan Sanchez, et al.
0

Robotic manipulation and control has increased in importance in recent years. However, state of the art techniques still have limitations when required to operate in real world applications. This paper explores Hindsight Experience Replay both in simulated and real environments, highlighting its weaknesses and proposing reinforcement-learning based alternatives based on reward and goal shaping. Additionally, several research questions are identified along with potential research directions that could be explored to tackle those questions.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/15/2023

Sim-to-Real Brush Manipulation using Behavior Cloning and Reinforcement Learning

Developing proficient brush manipulation capabilities in real-world scen...
research
07/28/2021

Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings

Learning continuous control in high-dimensional sparse reward settings, ...
research
10/07/2021

Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations

This paper deals with robotic lever control using Explainable Deep Reinf...
research
05/12/2021

An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet

This work re-implements the OpenAI Gym multi-goal robotic manipulation e...
research
10/03/2022

Hierarchical reinforcement learning for in-hand robotic manipulation using Davenport chained rotations

End-to-end reinforcement learning techniques are among the most successf...
research
10/02/2018

Energy-Based Hindsight Experience Prioritization

In Hindsight Experience Replay (HER), a reinforcement learning agent is ...
research
03/17/2021

An Inextensible Model for Robotic Simulations of Textiles

We introduce a new isometric strain model for the study of the dynamics ...

Please sign up or login with your details

Forgot password? Click here to reset