91 research outputs found
Poster: How to Raise a Robot - Beyond Access Control Constraints in Assistive Humanoid Robots
Humanoid robots will be able to assist humans in their daily life, in particular due to their versatile action capabilities. However, while these robots need a certain degree of autonomy to learn and explore, they also should respect various constraints, for access control and beyond. We explore incorporating privacy and security constraints (Activity-Centric Access Control and Deep Learning Based Access Control) with robot task planning approaches (classical symbolic planning and end-to-end learning-based planning). We report preliminary results on their respective trade-offs and conclude that a hybrid approach will most likely be the method of choice
Identifying Critical Regions for Robot Planning Using Convolutional Neural Networks
abstract: In this thesis, a new approach to learning-based planning is presented where critical regions of an environment with low probability measure are learned from a given set of motion plans. Critical regions are learned using convolutional neural networks (CNN) to improve sampling processes for motion planning (MP).
In addition to an identification network, a new sampling-based motion planner, Learn and Link, is introduced. This planner leverages critical regions to overcome the limitations of uniform sampling while still maintaining guarantees of correctness inherent to sampling-based algorithms. Learn and Link is evaluated against planners from the Open Motion Planning Library (OMPL) on an extensive suite of challenging navigation planning problems. This work shows that critical areas of an environment are learnable, and can be used by Learn and Link to solve MP problems with far less planning time than existing sampling-based planners.Dissertation/ThesisMasters Thesis Computer Science 201
Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning
Case-Based planning can fruitfully exploit knowledge
gained by solving a large number of problems, storing
the corresponding solutions in a plan library and reusing
them for solving similar planning problems in the future.
Case-based planning is extremely effective when
similar reuse candidates can be efficiently chosen.
In this paper, we study an innovative technique based
on planning problem features for efficiently retrieving
solved planning problems (and relative plans) from
large plan libraries. A problem feature is a characteristic
of the instance that can be automatically derived from
the problem specification, domain and search space
analyses, and different problem encodings.
Since the use of existing planning features are not always
able to effectively distinguish between problems
within the same planning domain, we introduce a new
class of features.
An experimental analysis in this paper shows that our
features-based retrieval approach can significantly improve
the performance of a state-of-the-art case-based
planning system
Value Iteration Networks on Multiple Levels of Abstraction
Learning-based methods are promising to plan robot motion without performing
extensive search, which is needed by many non-learning approaches. Recently,
Value Iteration Networks (VINs) received much interest since---in contrast to
standard CNN-based architectures---they learn goal-directed behaviors which
generalize well to unseen domains. However, VINs are restricted to small and
low-dimensional domains, limiting their applicability to real-world planning
problems.
To address this issue, we propose to extend VINs to representations with
multiple levels of abstraction. While the vicinity of the robot is represented
in sufficient detail, the representation gets spatially coarser with increasing
distance from the robot. The information loss caused by the decreasing
resolution is compensated by increasing the number of features representing a
cell. We show that our approach is capable of solving significantly larger 2D
grid world planning tasks than the original VIN implementation. In contrast to
a multiresolution coarse-to-fine VIN implementation which does not employ
additional descriptive features, our approach is capable of solving challenging
environments, which demonstrates that the proposed method learns to encode
useful information in the additional features. As an application for solving
real-world planning tasks, we successfully employ our method to plan
omnidirectional driving for a search-and-rescue robot in cluttered terrain
Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving
Reinforcement learning has received high research interest for developing
planning approaches in automated driving. Most prior works consider the
end-to-end planning task that yields direct control commands and rarely deploy
their algorithm to real vehicles. In this work, we propose a method to employ a
trained deep reinforcement learning policy for dedicated high-level behavior
planning. By populating an abstract objective interface, established motion
planning algorithms can be leveraged, which derive smooth and drivable
trajectories. Given the current environment model, we propose to use a built-in
simulator to predict the traffic scene for a given horizon into the future. The
behavior of automated vehicles in mixed traffic is determined by querying the
learned policy. To the best of our knowledge, this work is the first to apply
deep reinforcement learning in this manner, and as such lacks a
state-of-the-art benchmark. Thus, we validate the proposed approach by
comparing an idealistic single-shot plan with cyclic replanning through the
learned policy. Experiments with a real testing vehicle on proving grounds
demonstrate the potential of our approach to shrink the simulation to real
world gap of deep reinforcement learning based planning approaches. Additional
simulative analyses reveal that more complex multi-agent maneuvers can be
managed by employing the cycling replanning approach.Comment: 8 pages, 10 figures, to be published in 34th IEEE Intelligent
Vehicles Symposium (IV
Protected Area Planning Principles and Strategies
In this chapter, the challenges of protected area planning are explored by addressing the latter question. The chapter focuses on maintaining protected area values in face of increasing recreational pressure, although these general concepts and principles can be applied to other threats as well (Machlis and Tichnell 1985). First, the social and political contexts within which such planning occurs are outlined. It is to these complex contexts that an interactive, collaborative-learning based planning process would seem most appropriate. Next, an overview of eleven principles of visitor management is presented. These principles must be acknowledged and incorporated in any protected area planning system. Following this section, the conditions needed to implement a carrying capacity approach are reviewed; these requisite conditions lead us to conclude that, despite a resurgence of interest, the carrying capacity model does not adequately address the needs of protected area management. The final section briefly outlines the Limits of Acceptable Change planning system, an example of an approach that can incorporate the eleven previously described principles and has a demonstrated capacity to respond to the needs of protected area managers. The ideas in this chapter have been variously presented in Malaysia, Venezuela, Canada, and Puerto Rico (McCool 1996, McCool and Stankey 1992, Stankey and McCool 1993) and have benefited from the positive interactions and feedback received from protected area managers in those countries
ASAP: An Automatic Algorithm Selection Approach for Planning
Despite the advances made in the last decade in automated planning, no planner out-
performs all the others in every known benchmark domain. This observation motivates
the idea of selecting different planning algorithms for different domains. Moreover, the
plannersâ performances are affected by the structure of the search space, which depends
on the encoding of the considered domain. In many domains, the performance of a plan-
ner can be improved by exploiting additional knowledge, for instance, in the form of
macro-operators or entanglements.
In this paper we propose ASAP, an automatic Algorithm Selection Approach for
Planning that: (i) for a given domain initially learns additional knowledge, in the form
of macro-operators and entanglements, which is used for creating different encodings
of the given planning domain and problems, and (ii) explores the 2 dimensional space
of available algorithms, defined as encodingsâplanners couples, and then (iii) selects the
most promising algorithm for optimising either the runtimes or the quality of the solution
plans
An Automatic Algorithm Selection Approach for Planning
Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, extracted in the form of macro-operators or entanglements.
In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings--planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans
Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network
It is crucial to ask how agents can achieve goals by generating action plans
using only partial models of the world acquired through habituated
sensory-motor experiences. Although many existing robotics studies use a
forward model framework, there are generalization issues with high degrees of
freedom. The current study shows that the predictive coding (PC) and active
inference (AIF) frameworks, which employ a generative model, can develop better
generalization by learning a prior distribution in a low dimensional latent
state space representing probabilistic structures extracted from well
habituated sensory-motor trajectories. In our proposed model, learning is
carried out by inferring optimal latent variables as well as synaptic weights
for maximizing the evidence lower bound, while goal-directed planning is
accomplished by inferring latent variables for maximizing the estimated lower
bound. Our proposed model was evaluated with both simple and complex robotic
tasks in simulation, which demonstrated sufficient generalization in learning
with limited training data by setting an intermediate value for a
regularization coefficient. Furthermore, comparative simulation results show
that the proposed model outperforms a conventional forward model in
goal-directed planning, due to the learned prior confining the search of motor
plans within the range of habituated trajectories.Comment: 30 pages, 19 figure
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