Deep Predictive Policy Training using Reinforcement Learning

Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small sub-network with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.Comment: This work is submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems 2017 (IROS2017

From Human Physical Interaction To Online Motion Adaptation Using Parameterized Dynamical Systems

In this work, we present an adaptive motion planning approach for impedance-controlled robots to modify their tasks based on human physical interactions. We use a class of parameterized time-independent dynamical systems for motion generation where the modulation of such parameters allows for motion flexibility. To adapt to human interactions, we update the parameter of our dynamical system in order to reduce the tracking error (i.e., between the desired trajectory generated by the dynamical system and the real trajectory influenced by the human interaction). We provide analytical analysis and several simulations of our method. Finally, we investigate our approach through real world experiments with 7-DOF KUKA LWR 4+ robot performing tasks such as polishing and pick-and-place

Reinforcement Learning Approaches in Social Robotics

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field

Reinforcement learning for sequential decision-making: a data driven approach for finance

This work presents a variety of reinforcement learning applications to the domain of nance. It composes of two-part. The rst one represents a technical overview of the basic concepts in machine learning, which are required to understand and work with the reinforcement learning paradigm and are shared among the domains of applications. Chapter 1 outlines the fundamental principle of machine learning reasoning before introducing the neural network model as a central component of every algorithm presented in this work. Chapter 2 introduces the idea of reinforcement learning from its roots, focusing on the mathematical formalism generally employed in every application. We focus on integrating the reinforcement learning framework with the neural network, and we explain their critical role in the eld's development. After the technical part, we present our original contribution, articulated in three di erent essays. The narrative line follows the idea of introducing the use of varying reinforcement learning algorithms through a trading application (Brini and Tantari, 2021) in Chapter 3. Then in Chapter 4 we focus on one of the presented reinforcement learning algorithms and aim at improving its performance and scalability in solving the trading problem by leveraging prior knowledge of the setting. In Chapter 5 of the second part, we use the same reinforcement learning algorithm to solve the problem of exchanging liquidity in a system of banks that can borrow and lend money, highlighting the exibility and the e ectiveness of the reinforcement learning paradigm in the broad nancial domain. We conclude with some remarks and ideas for further research in reinforcement learning applied to nance