Deep Reinforcement Learning for Robotic Manipulation-The state of the art

TL;DR

This paper surveys recent deep reinforcement learning algorithms for robotic manipulation, highlighting their architectures, applications, and distinctions between discrete and continuous action spaces, both in simulation and real-world settings.

Contribution

It provides a comprehensive overview of the latest DRL methods, categorizing them and discussing their implementation in robotic manipulation tasks.

Findings

Deep RL enables more generalized robotic policies.

Recent algorithms effectively integrate perception and control.

Applications span simulation and real-world robotic platforms.

Abstract

The focus of this work is to enumerate the various approaches and algorithms that center around application of reinforcement learning in robotic ma- ]]nipulation tasks. Earlier methods utilized specialized policy representations and human demonstrations to constrict the policy. Such methods worked well with continuous state and policy space of robots but failed to come up with generalized policies. Subsequently, high dimensional non-linear function approximators like neural networks have been used to learn policies from scratch. Several novel and recent approaches have also embedded control policy with efficient perceptual representation using deep learning. This has led to the emergence of a new branch of dynamic robot control system called deep r inforcement learning(DRL). This work embodies a survey of the most recent algorithms, architectures and their implementations in simulations and real world robotic platforms. The gamut of DRL architectures are partitioned into two different branches namely, discrete action space algorithms(DAS) and continuous action space algorithms(CAS). Further, the CAS algorithms are divided into stochastic continuous action space(SCAS) and deterministic continuous action space(DCAS) algorithms. Along with elucidating an organ- isation of the DRL algorithms this work also manifests some of the state of the art applications of these approaches in robotic manipulation tasks.