102,191 research outputs found
Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees
Sampling efficiency in a highly constrained environment has long been a major
challenge for sampling-based planners. In this work, we propose
Rapidly-exploring Random disjointed-Trees* (RRdT*), an incremental optimal
multi-query planner. RRdT* uses multiple disjointed-trees to exploit
local-connectivity of spaces via Markov Chain random sampling, which utilises
neighbourhood information derived from previous successful and failed samples.
To balance local exploitation, RRdT* actively explore unseen global spaces when
local-connectivity exploitation is unsuccessful. The active trade-off between
local exploitation and global exploration is formulated as a multi-armed bandit
problem. We argue that the active balancing of global exploration and local
exploitation is the key to improving sample efficient in sampling-based motion
planners. We provide rigorous proofs of completeness and optimal convergence
for this novel approach. Furthermore, we demonstrate experimentally the
effectiveness of RRdT*'s locally exploring trees in granting improved
visibility for planning. Consequently, RRdT* outperforms existing
state-of-the-art incremental planners, especially in highly constrained
environments.Comment: Submitted to IEEE International Conference on Robotics and Automation
(ICRA) 201
Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation
In this paper, we present a novel method for achieving dexterous manipulation
of complex objects, while simultaneously securing the object without the use of
passive support surfaces. We posit that a key difficulty for training such
policies in a Reinforcement Learning framework is the difficulty of exploring
the problem state space, as the accessible regions of this space form a complex
structure along manifolds of a high-dimensional space. To address this
challenge, we use two versions of the non-holonomic Rapidly-Exploring Random
Trees algorithm; one version is more general, but requires explicit use of the
environment's transition function, while the second version uses
manipulation-specific kinematic constraints to attain better sample efficiency.
In both cases, we use states found via sampling-based exploration to generate
reset distributions that enable training control policies under full dynamic
constraints via model-free Reinforcement Learning. We show that these policies
are effective at manipulation problems of higher difficulty than previously
shown, and also transfer effectively to real robots. Videos of the real-hand
demonstrations can be found on the project website:
https://sbrl.cs.columbia.edu/Comment: 10 pages, 6 figures, submitted to Robotics Science & Systems 202
Simulation of Rapidly-Exploring Random Trees in Membrane Computing with P-Lingua and Automatic Programming
Methods based on Rapidly-exploring Random Trees (RRTs) have been
widely used in robotics to solve motion planning problems. On the other hand, in the
membrane computing framework, models based on Enzymatic Numerical P systems
(ENPS) have been applied to robot controllers, but today there is a lack of planning
algorithms based on membrane computing for robotics. With this motivation, we
provide a variant of ENPS called Random Enzymatic Numerical P systems with
Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms
and we illustrate it by simulating the bidirectional RRT algorithm. This paper is an
extension of [21]a. The software presented in [21] was an ad-hoc simulator, i.e, a tool
for simulating computations of one and only one model that has been hard-coded.
The main contribution of this paper with respect to [21] is the introduction of a novel
solution for membrane computing simulators based on automatic programming. First,
we have extended the P-Lingua syntax –a language to define membrane computing
models– to write RENPSM models. Second, we have implemented a new parser based
on Flex and Bison to read RENPSM models and produce source code in C language
for multicore processors with OpenMP. Finally, additional experiments are presented.Ministerio de EconomĂa, Industria y Competitividad TIN2017-89842-
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