332 research outputs found
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space
Combining model-based and model-free deep reinforcement learning has shown
great promise for improving sample efficiency on complex control tasks while
still retaining high performance. Incorporating imagination is a recent effort
in this direction inspired by human mental simulation of motor behavior. We
propose a learning-adaptive imagination approach which, unlike previous
approaches, takes into account the reliability of the learned dynamics model
used for imagining the future. Our approach learns an ensemble of disjoint
local dynamics models in latent space and derives an intrinsic reward based on
learning progress, motivating the controller to take actions leading to data
that improves the models. The learned models are used to generate imagined
experiences, augmenting the training set of real experiences. We evaluate our
approach on learning vision-based robotic grasping and show that it
significantly improves sample efficiency and achieves near-optimal performance
in a sparse reward environment.Comment: In: Proceedings of the Joint IEEE International Conference on
Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), Oslo,
Norway, Aug. 19-22, 201
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
We present RoboGen, a generative robotic agent that automatically learns
diverse robotic skills at scale via generative simulation. RoboGen leverages
the latest advancements in foundation and generative models. Instead of
directly using or adapting these models to produce policies or low-level
actions, we advocate for a generative scheme, which uses these models to
automatically generate diversified tasks, scenes, and training supervisions,
thereby scaling up robotic skill learning with minimal human supervision. Our
approach equips a robotic agent with a self-guided propose-generate-learn
cycle: the agent first proposes interesting tasks and skills to develop, and
then generates corresponding simulation environments by populating pertinent
objects and assets with proper spatial configurations. Afterwards, the agent
decomposes the proposed high-level task into sub-tasks, selects the optimal
learning approach (reinforcement learning, motion planning, or trajectory
optimization), generates required training supervision, and then learns
policies to acquire the proposed skill. Our work attempts to extract the
extensive and versatile knowledge embedded in large-scale models and transfer
them to the field of robotics. Our fully generative pipeline can be queried
repeatedly, producing an endless stream of skill demonstrations associated with
diverse tasks and environments
The active inference approach to ecological perception: general information dynamics for natural and artificial embodied cognition
The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents—who shape and are shaped by their environment—offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework (AIF) makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a naturalistic and information-theoretic foundation, using the principle of free energy minimization. The latter provides a theoretical basis for a unified treatment of particles, organisms, and interactive machines, spanning from the inorganic to organic, non-life to life, and natural to artificial agents. We provide a brief introduction to AIF, then explore its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics/AI research. We conclude the paper by considering implications for machine consciousness
Deep Reinforcement Learning: A Brief Survey
Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems that were previously intractable, such as learning to play video games directly from pixels. DRL algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of RL, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via RL. To conclude, we describe several current areas of research within the field
Causal Reinforcement Learning: A Survey
Reinforcement learning is an essential paradigm for solving sequential
decision problems under uncertainty. Despite many remarkable achievements in
recent decades, applying reinforcement learning methods in the real world
remains challenging. One of the main obstacles is that reinforcement learning
agents lack a fundamental understanding of the world and must therefore learn
from scratch through numerous trial-and-error interactions. They may also face
challenges in providing explanations for their decisions and generalizing the
acquired knowledge. Causality, however, offers a notable advantage as it can
formalize knowledge in a systematic manner and leverage invariance for
effective knowledge transfer. This has led to the emergence of causal
reinforcement learning, a subfield of reinforcement learning that seeks to
enhance existing algorithms by incorporating causal relationships into the
learning process. In this survey, we comprehensively review the literature on
causal reinforcement learning. We first introduce the basic concepts of
causality and reinforcement learning, and then explain how causality can
address core challenges in non-causal reinforcement learning. We categorize and
systematically review existing causal reinforcement learning approaches based
on their target problems and methodologies. Finally, we outline open issues and
future directions in this emerging field.Comment: 48 pages, 10 figure
The Future of Humanoid Robots
This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book
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