3,482 research outputs found
Learning Probabilistic Generative Models For Fast Sampling-Based Planning
Due to their simplicity and efficiency in high dimensional space, sampling-based motion planners have been gaining interest for robotic manipulation in recent years. We present several new learning approaches using probabilistic generative models for fast sampling-based planning. First, we propose fast collision detection in high dimensional configuration spaces based on Gaussian Mixture Models (GMMs) for Rapidly-exploring Random Trees (RRT). In addition, we introduce a new probabilistically safe local steering primitive based on the probabilistic model. Our local steering procedure is based on a new notion of a convex probabilistically safety corridor that is constructed around a configuration using tangent hyperplanes of confidence ellipsoids of GMMs learned from prior collision history. For efficient sampling, we suggest a sampling method with a learned Q-function with linear function approximation based on feature representations such as Radial Basis Functions. This sampling method chooses the optimal node from which to extend the search tree via the softmax function of learned state values. We also discuss a novel constrained sampling-based motion planning method for grasp and transport tasks with redundant robotic manipulators, which allows the best grasp configuration and approach direction to be automatically determined. Since these approaches with the learned probabilistic models require large size data and time for training, it is essential that they are able to be adapted to environmental change in an online manner. The suggested online learning approach with the Dirichlet Process Mixture Model (DPMM) can adapt the complexity to the data and learn new Gaussian clusters with streaming data in newly explored areas without batch learning. We have applied these approaches in a number of robot arm planning scenarios and have shown their utility and effectiveness in simulation and on a physical 7-DoF robot manipulator
Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning
When facing a new motion-planning problem, most motion planners solve it from
scratch, e.g., via sampling and exploration or starting optimization from a
straight-line path. However, most motion planners have to experience a variety
of planning problems throughout their lifetimes, which are yet to be leveraged
for future planning. In this paper, we present a simple but efficient method
called Motion Memory, which allows different motion planners to accelerate
future planning using past experiences. Treating existing motion planners as
either a closed or open box, we present a variety of ways that Motion Memory
can contribute to reduce the planning time when facing a new planning problem.
We provide extensive experiment results with three different motion planners on
three classes of planning problems with over 30,000 problem instances and show
that planning speed can be significantly reduced by up to 89% with the proposed
Motion Memory technique and with increasing past planning experiences
Bayesian Active Edge Evaluation on Expensive Graphs
Robots operate in environments with varying implicit structure. For instance,
a helicopter flying over terrain encounters a very different arrangement of
obstacles than a robotic arm manipulating objects on a cluttered table top.
State-of-the-art motion planning systems do not exploit this structure, thereby
expending valuable planning effort searching for implausible solutions. We are
interested in planning algorithms that actively infer the underlying structure
of the valid configuration space during planning in order to find solutions
with minimal effort. Consider the problem of evaluating edges on a graph to
quickly discover collision-free paths. Evaluating edges is expensive, both for
robots with complex geometries like robot arms, and for robots with limited
onboard computation like UAVs. Until now, this challenge has been addressed via
laziness i.e. deferring edge evaluation until absolutely necessary, with the
hope that edges turn out to be valid. However, all edges are not alike in value
- some have a lot of potentially good paths flowing through them, and some
others encode the likelihood of neighbouring edges being valid. This leads to
our key insight - instead of passive laziness, we can actively choose edges
that reduce the uncertainty about the validity of paths. We show that this is
equivalent to the Bayesian active learning paradigm of decision region
determination (DRD). However, the DRD problem is not only combinatorially hard,
but also requires explicit enumeration of all possible worlds. We propose a
novel framework that combines two DRD algorithms, DIRECT and BISECT, to
overcome both issues. We show that our approach outperforms several
state-of-the-art algorithms on a spectrum of planning problems for mobile
robots, manipulators and autonomous helicopters
Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model
The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples' rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (EM) algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency
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