How To Train Your Robot New Environments For Robotic Training And New Methods For Transferring Policies From The Simulator To The Real Robot
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How to Train Your Robot. New Environments for Robotic Training and New Methods for Transferring Policies from the Simulator to the Real Robot
Robots are the future. But how can we teach them useful new skills? This work covers a variety of topics, all with the common goal of making it easier to train robots. The first main component of this thesis is our work on model-building sim2real transfer. When a policy has been learned entirely in simulation, the performance of this policy is usually drastically lower on the real robot. This can be due to random noise, to imprecisions, or to unmodelled effects like backlash. We introduce a new technique for learning the discrepancy between the simulator and the real robot and using this discrepancy to correct the simulator. We found that for several of our ideas there weren't any suitable simulations available. Therefore, for the second main part of the thesis, we created a set of new robotic simulation and test environments. We provide (1) several new robot simulations for existing robots and variations on existing environments that allow for rapid adjustment of the robot dynamics. We also co-created (2) the Duckietown AIDO challenge, which is a large scale live robotics competition for the conferences NIPS 2018 and ICRA 2019. For this challenge we created the simulation infrastructure, which allows participants to train their robots in simulation with or without ROS. It also lets them evaluate their submissions automatically on live robots in a ”Robotarium”. In order to evaluate a robot's understanding and continuous acquisition of language, we developed the (3) Multimodal Human-Robot Interaction benchmark (MHRI). This test set contains several hours of annotated recordings of different humans showing and pointing at common household items, all from a robot's perspective. The novelty and difficulty in this task stems from the realistic noise that is included in the dataset: Most humans were non-native English speakers, some objects were occluded and none of the humans were given any detailed instructions on how to communicate with the robot, resulting in very natural interactions. After completing this benchmark, we realized the lack of simulation environments that are sufficiently complex to train a robot for this task. This would require an agent in a realistic house settings with semantic annotations. That is why we created (4) HoME, a platform for training household robots to understand language. The environment was created by wrapping the existing SUNCG 3D database of houses in a game engine to allow simulated agents to traverse the houses. It integrates a highly-detailed acoustic engine and a semantic engine that can generate object descriptions in relation to other objects, furniture, and rooms. The third and final main contribution of this work considered that a robot might find itself in a novel environment which wasn't covered by the simulation. For such a case we provide a new approach that allows the agent to reconstruct a 3D scene from 2D images by learning object embeddings, since especially in low-cost robots a depth sensor is not always available, but 2D cameras a common. The main drawback of this work is that it currently doesn't reliably support reconstruction of color or texture. We tested the approach on a mental rotation task, which is common in IQ tests, and found that our model performs significantly better in recognizing and rotating objects than several baselines.
Make: Volume 93
Humanoid robots aren’t just for mega-corps and secretive startups. In this issue of Make:, we show you how to use AI programs and open source plans to experiment and build your own humanoid helpers right now! In our cover story, build VoxHead, a fully animated, embodied AI, humanoid head from scratch. Then, we catch up with Gael Langevin about the continuing evolution of open source humanoid InMoov: new facial expressions, integrated AI, and even synthetic skin! Plus, humanoid robots need a trusty canine companion — build a cute, athletic, quadruped pupper with an AI chatbot brain and powerful QDD actuators. But how do we make all these futuristic robots move? Dive into our primer on field-oriented control for brushless motors, the tech that lets bots run and jump like never before. Then, we revisit our ultimate maker tools for your workshop. The kicker: a pie-in-the-sky workshop from 20 years ago is now affordable for makers! But our visit to Lawrence Berkeley National Labs also shows there’s always a crazier workshop out there. Plus 17 projects, including: Construct a tiny houseboat for day trips and camping that packs down to fit in an SUV Use inverse kinematics to give a robot arm sketchbot pinpoint accuracy Fly a lively, no-sew kite using Tyvek fabric and 3D-printed connectors Block-print computational moiré patterns with Open Press Project and p5.js. Build a laser communicator using logic chips to send secret codes securely Make flexible pushbuttons and switches for wearable electronics Assemble a 100W fast-charging battery bank using lithium cells salvaged from disposable vapes And much more!
Artificial Neural Nets and Genetic Algorithms
Author: George D. Smith
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
This is the third in a series of conferences devoted primarily to the theory and applications of artificial neural networks and genetic algorithms. The first such event was held in Innsbruck, Austria, in April 1993, the second in Ales, France, in April 1995. We are pleased to host the 1997 event in the mediaeval city of Norwich, England, and to carryon the fine tradition set by its predecessors of providing a relaxed and stimulating environment for both established and emerging researchers working in these and other, related fields. This series of conferences is unique in recognising the relation between the two main themes of artificial neural networks and genetic algorithms, each having its origin in a natural process fundamental to life on earth, and each now well established as a paradigm fundamental to continuing technological development through the solution of complex, industrial, commercial and financial problems. This is well illustrated in this volume by the numerous applications of both paradigms to new and challenging problems. The third key theme of the series, therefore, is the integration of both technologies, either through the use of the genetic algorithm to construct the most effective network architecture for the problem in hand, or, more recently, the use of neural networks as approximate fitness functions for a genetic algorithm searching for good solutions in an 'incomplete' solution space, i.e. one for which the fitness is not easily established for every possible solution instance.