Skip to Main content Skip to Navigation
Theses

Data-Efficient Robot Learning using Priors from Simulators

Rituraj Kaushik 1
1 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : As soon as the robots step out in the real and uncertain world, they have to adapt to various unanticipated situations by acquiring new skills as quickly as possible. Unfortunately, on robots, current state-of-the-art reinforcement learning (e.g., deep-reinforcement learning) algorithms require large interaction time to train a new skill. In this thesis, we have explored methods to allow a robot to acquire new skills through trial-and-error within a few minutes of physical interaction. Our primary focus is to incorporate prior knowledge from a simulator with real-world experiences of a robot to achieve rapid learning and adaptation. In our first contribution, we propose a novel model-based policy search algorithm called Multi-DEX that (1) is capable of finding policies in sparse reward scenarios (2) does not impose any constraints on the type of policy or the type of reward function and (3) is as data-efficient as state-of-the-art model-based policy search algorithm in non-sparse reward scenarios. In our second contribution, we propose a repertoire-based online learning algorithm called APROL which allows a robot to adapt to physical damages (e.g., a damaged leg) or environmental perturbations (e.g., terrain conditions) quickly and solve the given task. In this work, we use several repertoires of policies generated in simulation for a subset of possible situations that the robot might face in real-world. During the online learning, the robot automatically figures out the most suitable repertoire to adapt and control the robot. We show that APROL outperforms several baselines including the current state-of-the-art repertoire-based learning algorithm RTE by solving the tasks in much less interaction times than the baselines. In our third contribution, we introduce a gradient-based meta-learning algorithm called FAMLE. FAMLE meta-trains the dynamical model of the robot from simulated data so that the model can be adapted to various unseen situations quickly with the real-world observations. By using FAMLE with a model-predictive control framework, we show that our approach outperforms several model-based and model-free learning algorithms, and solves the given tasks in less interaction time than the baselines.
Complete list of metadatas

Cited literature [220 references]  Display  Hide  Download

https://hal.univ-lorraine.fr/tel-02976390
Contributor : Thèses Ul <>
Submitted on : Friday, October 23, 2020 - 2:19:59 PM
Last modification on : Saturday, October 24, 2020 - 3:37:59 AM

File

DDOC_T_2020_0105_KAUSHIK.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : tel-02976390, version 1

Citation

Rituraj Kaushik. Data-Efficient Robot Learning using Priors from Simulators. Artificial Intelligence [cs.AI]. Université de Lorraine, 2020. English. ⟨NNT : 2020LORR0105⟩. ⟨tel-02976390⟩

Share

Metrics

Record views

72

Files downloads

64