332 research outputs found
Composable Deep Reinforcement Learning for Robotic Manipulation
Model-free deep reinforcement learning has been shown to exhibit good
performance in domains ranging from video games to simulated robotic
manipulation and locomotion. However, model-free methods are known to perform
poorly when the interaction time with the environment is limited, as is the
case for most real-world robotic tasks. In this paper, we study how maximum
entropy policies trained using soft Q-learning can be applied to real-world
robotic manipulation. The application of this method to real-world manipulation
is facilitated by two important features of soft Q-learning. First, soft
Q-learning can learn multimodal exploration strategies by learning policies
represented by expressive energy-based models. Second, we show that policies
learned with soft Q-learning can be composed to create new policies, and that
the optimality of the resulting policy can be bounded in terms of the
divergence between the composed policies. This compositionality provides an
especially valuable tool for real-world manipulation, where constructing new
policies by composing existing skills can provide a large gain in efficiency
over training from scratch. Our experimental evaluation demonstrates that soft
Q-learning is substantially more sample efficient than prior model-free deep
reinforcement learning methods, and that compositionality can be performed for
both simulated and real-world tasks.Comment: Videos: https://sites.google.com/view/composing-real-world-policies
Indirect Methods for Robot Skill Learning
Robot learning algorithms are appealing alternatives for acquiring rational robotic behaviors from data collected during the execution of tasks. Furthermore, most robot learning techniques are stated as isolated stages and focused on directly obtaining rational policies as a result of optimizing only performance measures of single tasks. However, formulating robotic skill acquisition processes in such a way have some disadvantages. For example, if the same skill has to be learned by different robots, independent learning processes should be carried out for acquiring exclusive policies for each robot. Similarly, if a robot has to learn diverse skills, the robot should acquire the policy for each task in separate learning processes, in a sequential order and commonly starting from scratch. In the same way, formulating the learning process in terms of only the performance measure, makes robots to unintentionally avoid situations that should not be repeated, but without any mechanism that captures the necessity of not repeating those wrong behaviors. In contrast, humans and other animals exploit their experience not only for improving the performance of the task they are currently executing, but for constructing indirectly multiple models to help them with that particular task and to generalize to new problems. Accordingly, the models and algorithms proposed in this thesis seek to be more data efficient and extract more information from the interaction data that is collected either from expert\u2019s demonstrations or the robot\u2019s own experience. The first approach encodes robotic skills with shared latent variable models, obtaining latent representations that can be transferred from one robot to others, therefore avoiding to learn the same task from scratch. The second approach learns complex rational policies by representing them as hierarchical models that can perform multiple concurrent tasks, and whose components are learned in the same learning process, instead of separate processes. Finally, the third approach uses the interaction data for learning two alternative and antagonistic policies that capture what to and not to do, and which influence the learning process in addition to the performance measure defined for the task
Differentiable Algorithm Networks for Composable Robot Learning
This paper introduces the Differentiable Algorithm Network (DAN), a
composable architecture for robot learning systems. A DAN is composed of neural
network modules, each encoding a differentiable robot algorithm and an
associated model; and it is trained end-to-end from data. DAN combines the
strengths of model-driven modular system design and data-driven end-to-end
learning. The algorithms and models act as structural assumptions to reduce the
data requirements for learning; end-to-end learning allows the modules to adapt
to one another and compensate for imperfect models and algorithms, in order to
achieve the best overall system performance. We illustrate the DAN methodology
through a case study on a simulated robot system, which learns to navigate in
complex 3-D environments with only local visual observations and an image of a
partially correct 2-D floor map.Comment: RSS 2019 camera ready. Video is available at
https://youtu.be/4jcYlTSJF4
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