302,449 research outputs found
Learning Generalized Reactive Policies using Deep Neural Networks
We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3
Active model learning and diverse action sampling for task and motion planning
The objective of this work is to augment the basic abilities of a robot by
learning to use new sensorimotor primitives to enable the solution of complex
long-horizon problems. Solving long-horizon problems in complex domains
requires flexible generative planning that can combine primitive abilities in
novel combinations to solve problems as they arise in the world. In order to
plan to combine primitive actions, we must have models of the preconditions and
effects of those actions: under what circumstances will executing this
primitive achieve some particular effect in the world?
We use, and develop novel improvements on, state-of-the-art methods for
active learning and sampling. We use Gaussian process methods for learning the
conditions of operator effectiveness from small numbers of expensive training
examples collected by experimentation on a robot. We develop adaptive sampling
methods for generating diverse elements of continuous sets (such as robot
configurations and object poses) during planning for solving a new task, so
that planning is as efficient as possible. We demonstrate these methods in an
integrated system, combining newly learned models with an efficient
continuous-space robot task and motion planner to learn to solve long horizon
problems more efficiently than was previously possible.Comment: Proceedings of the 2018 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS), Madrid, Spain.
https://www.youtube.com/playlist?list=PLoWhBFPMfSzDbc8CYelsbHZa1d3uz-W_
Planning in action language BC while learning action costs for mobile robots
The action language BC provides an elegant way of formalizing dynamic domains which involve indirect effects of actions and recursively defined fluents. In complex robot task planning domains, it may be necessary for robots to plan with incomplete information, and reason about indirect or recursive action effects. In this paper, we demonstrate how BC can be used for robot task planning to solve these issues. Additionally, action costs are incorporated with planning to produce optimal plans, and we estimate these costs from experience making planning adaptive. This paper presents the first application of BC on a real robot in a realistic domain, which involves human-robot interaction for knowledge acquisition, optimal plan generation to minimize navigation time, and learning for adaptive planning
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Question Asking During Procedural Learning: Strategies for Acquiring Knowledge in Several Domains
Questions asked during acquisition of a complex skill reflect the types of knowledge that learners require at different stages. Questions that learners ask themselves may serve to generate incomplete conceptual frames that can be used to guide explanation of future events. Question asking data collected from students learning to use a spread sheet program suggest that learners initially require knowledge about plans and the structure of the skill domain.Next they require knowledge about the structure of tasks that they will be performing. Finally they concentrate on plan refinement. Models of skill acquisition and explanation-based learning should incorporate mechanisms for monitoring levels of knowledge in several distinct domains and dynamically altering strategies for knowledge acquisition within these domains
Skill-based Model-based Reinforcement Learning
Model-based reinforcement learning (RL) is a sample-efficient way of learning
complex behaviors by leveraging a learned single-step dynamics model to plan
actions in imagination. However, planning every action for long-horizon tasks
is not practical, akin to a human planning out every muscle movement. Instead,
humans efficiently plan with high-level skills to solve complex tasks. From
this intuition, we propose a Skill-based Model-based RL framework (SkiMo) that
enables planning in the skill space using a skill dynamics model, which
directly predicts the skill outcomes, rather than predicting all small details
in the intermediate states, step by step. For accurate and efficient long-term
planning, we jointly learn the skill dynamics model and a skill repertoire from
prior experience. We then harness the learned skill dynamics model to
accurately simulate and plan over long horizons in the skill space, which
enables efficient downstream learning of long-horizon, sparse reward tasks.
Experimental results in navigation and manipulation domains show that SkiMo
extends the temporal horizon of model-based approaches and improves the sample
efficiency for both model-based RL and skill-based RL. Code and videos are
available at \url{https://clvrai.com/skimo}Comment: Website: \url{https://clvrai.com/skimo
Improving the Performance of Complex Agent Plans Through Reinforcement Learning
Agent programming in complex, partially observable and stochastic domains usually requires a great deal of understanding of both the domain and the task, in order to provide the agent with the knowledge necessary to act effectively. While symbolic methods allow the designer to specify declarative knowledge about the domain, the resulting plan can be brittle since it is difficult to supply a symbolic model that is accurate enough to foresee all possible events in complex environments, especially in the case of partial observability. Reinforcement Learning (RL) techniques, on the other hand, can learn a policy and make use of a learned model, but it is difficult to reduce and shape the scope of the learning algorithm by exploiting a priori information. We propose a methodology for writing complex agent programs that can be effectively improved through experience. We show how to derive a stochastic process from a partial specification of the plan, so that the latter's perfomance can be improved solving a RL problem much smaller than classical RL formulations. Finally, we demonstrate our approach in the context of Keepaway Soccer, a common RL benchmark based on a RoboCup Soccer 2D simulator. Copyright © 2010, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved
Learning Symbolic Operators for Task and Motion Planning
Robotic planning problems in hybrid state and action spaces can be solved by
integrated task and motion planners (TAMP) that handle the complex interaction
between motion-level decisions and task-level plan feasibility. TAMP approaches
rely on domain-specific symbolic operators to guide the task-level search,
making planning efficient. In this work, we formalize and study the problem of
operator learning for TAMP. Central to this study is the view that operators
define a lossy abstraction of the transition model of a domain. We then propose
a bottom-up relational learning method for operator learning and show how the
learned operators can be used for planning in a TAMP system. Experimentally, we
provide results in three domains, including long-horizon robotic planning
tasks. We find our approach to substantially outperform several baselines,
including three graph neural network-based model-free approaches from the
recent literature. Video: https://youtu.be/iVfpX9BpBRo Code:
https://git.io/JCT0gComment: IROS 202
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