A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

Abstract

A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges.

References in corpus (21)

• Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
• Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects
• FeUdal Networks for Hierarchical Reinforcement Learning
• Go-Explore: a New Approach for Hard-Exploration Problems
• Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
• RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter
• Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
• Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
• Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
• Grasp2Vec: Learning Object Representations from Self-Supervised Grasping
• Safe Policy Improvement by Minimizing Robust Baseline Regret
• One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks
• Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
• Reasoning About Physical Interactions with Object-Oriented Prediction and Planning
• End-to-End Learning of Semantic Grasping
• Few-Shot Goal Inference for Visuomotor Learning and Planning
• Learning Robotic Manipulation of Granular Media
• Hybrid Bayesian Eigenobjects: Combining Linear Subspace and Deep Network Methods for 3D Robot Vision
• Unsupervised Learning for Nonlinear PieceWise Smooth Hybrid Systems
• Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks
• Hybrid DDP in Clutter (CHDDP): Trajectory Optimization for Hybrid Dynamical System in Cluttered Environments

Cited by in corpus (20)

• How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
• Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots
• Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
• Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks
• Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
• Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations
• A Survey of Embodied Learning for Object-Centric Robotic Manipulation
• A Survey on the Integration of Machine Learning with Sampling-based Motion Planning
• Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
• Experience-Embedded Visual Foresight
• PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards
• Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation
• Differentiable Physics-based System Identification for Robotic Manipulation of Elastoplastic Materials
• Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation
• Pixyz: a Python library for developing deep generative models
• Learning to Play by Imitating Humans
• Dataset Bias Mitigation Through Analysis of CNN Training Scores
• Automatic Failure Recovery for End-User Programs on Service Mobile Robots
• CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context
• An Adaptive Framework for Reliable Trajectory Following in Changing-Contact Robot Manipulation Tasks