76 research outputs found
Motion Planning Networks
Fast and efficient motion planning algorithms are crucial for many
state-of-the-art robotics applications such as self-driving cars. Existing
motion planning methods become ineffective as their computational complexity
increases exponentially with the dimensionality of the motion planning problem.
To address this issue, we present Motion Planning Networks (MPNet), a neural
network-based novel planning algorithm. The proposed method encodes the given
workspaces directly from a point cloud measurement and generates the end-to-end
collision-free paths for the given start and goal configurations. We evaluate
MPNet on various 2D and 3D environments including the planning of a 7 DOF
Baxter robot manipulator. The results show that MPNet is not only consistently
computationally efficient in all environments but also generalizes to
completely unseen environments. The results also show that the computation time
of MPNet consistently remains less than 1 second in all presented experiments,
which is significantly lower than existing state-of-the-art motion planning
algorithms.Comment: Paper published in ICRA'1
Constrained Motion Planning Networks X
Constrained motion planning is a challenging field of research, aiming for
computationally efficient methods that can find a collision-free path on the
constraint manifolds between a given start and goal configuration. These
planning problems come up surprisingly frequently, such as in robot
manipulation for performing daily life assistive tasks. However, few solutions
to constrained motion planning are available, and those that exist struggle
with high computational time complexity in finding a path solution on the
manifolds. To address this challenge, we present Constrained Motion Planning
Networks X (CoMPNetX). It is a neural planning approach, comprising a
conditional deep neural generator and discriminator with neural gradients-based
fast projection operator. We also introduce neural task and scene
representations conditioned on which the CoMPNetX generates implicit manifold
configurations to turbo-charge any underlying classical planner such as
Sampling-based Motion Planning methods for quickly solving complex constrained
planning tasks. We show that our method finds path solutions with high success
rates and lower computation times than state-of-the-art traditional
path-finding tools on various challenging scenarios.Comment: This is preprint version of a paper published in IEEE Transactions on
Robotics. The videos, code, dataset and trained models can be found here:
https://sites.google.com/view/compnetx/hom
Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots
Reliable real-time planning for robots is essential in today's rapidly
expanding automated ecosystem. In such environments, traditional methods that
plan by relaxing constraints become unreliable or slow-down for kinematically
constrained robots. This paper describes the algorithm Dynamic Motion Planning
Networks (Dynamic MPNet), an extension to Motion Planning Networks, for
non-holonomic robots that address the challenge of real-time motion planning
using a neural planning approach. We propose modifications to the training and
planning networks that make it possible for real-time planning while improving
the data efficiency of training and trained models' generalizability. We
evaluate our model in simulation for planning tasks for a non-holonomic robot.
We also demonstrate experimental results for an indoor navigation task using a
Dubins car.Comment: Accepted for IROS 202
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
This paper describes Motion Planning Networks (MPNet), a computationally
efficient, learning-based neural planner for solving motion planning problems.
MPNet uses neural networks to learn general near-optimal heuristics for path
planning in seen and unseen environments. It takes environment information such
as raw point-cloud from depth sensors, as well as a robot's initial and desired
goal configurations and recursively calls itself to bidirectionally generate
connectable paths. In addition to finding directly connectable and near-optimal
paths in a single pass, we show that worst-case theoretical guarantees can be
proven if we merge this neural network strategy with classical sample-based
planners in a hybrid approach while still retaining significant computational
and optimality improvements. To train the MPNet models, we present an active
continual learning approach that enables MPNet to learn from streaming data and
actively ask for expert demonstrations when needed, drastically reducing data
for training. We validate MPNet against gold-standard and state-of-the-art
planning methods in a variety of problems from 2D to 7D robot configuration
spaces in challenging and cluttered environments, with results showing
significant and consistently stronger performance metrics, and motivating
neural planning in general as a modern strategy for solving motion planning
problems efficiently.Comment: Supplementary material including implementation parameters and
project videos are available at https://sites.google.com/view/mpnet/home.
This work has been accepted for publication at IEEE Transactions on Robotic
MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints
Kinodynamic Motion Planning (KMP) is to find a robot motion subject to
concurrent kinematics and dynamics constraints. To date, quite a few methods
solve KMP problems and those that exist struggle to find near-optimal solutions
and exhibit high computational complexity as the planning space dimensionality
increases. To address these challenges, we present a scalable, imitation
learning-based, Model-Predictive Motion Planning Networks framework that
quickly finds near-optimal path solutions with worst-case theoretical
guarantees under kinodynamic constraints for practical underactuated systems.
Our framework introduces two algorithms built on a neural generator,
discriminator, and a parallelizable Model Predictive Controller (MPC). The
generator outputs various informed states towards the given target, and the
discriminator selects the best possible subset from them for the extension. The
MPC locally connects the selected informed states while satisfying the given
constraints leading to feasible, near-optimal solutions. We evaluate our
algorithms on a range of cluttered, kinodynamically constrained, and
underactuated planning problems with results indicating significant
improvements in computation times, path qualities, and success rates over
existing methods
Neural Manipulation Planning on Constraint Manifolds
The presence of task constraints imposes a significant challenge to motion
planning. Despite all recent advancements, existing algorithms are still
computationally expensive for most planning problems. In this paper, we present
Constrained Motion Planning Networks (CoMPNet), the first neural planner for
multimodal kinematic constraints. Our approach comprises the following
components: i) constraint and environment perception encoders; ii) neural robot
configuration generator that outputs configurations on/near the constraint
manifold(s), and iii) a bidirectional planning algorithm that takes the
generated configurations to create a feasible robot motion trajectory. We show
that CoMPNet solves practical motion planning tasks involving both
unconstrained and constrained problems. Furthermore, it generalizes to new
unseen locations of the objects, i.e., not seen during training, in the given
environments with high success rates. When compared to the state-of-the-art
constrained motion planning algorithms, CoMPNet outperforms by order of
magnitude improvement in computational speed with a significantly lower
variance.Comment: This is the preprint version of the paper published at IEEE Robotics
and Automation Letters 202
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
We propose a meta path planning algorithm named \emph{Neural
Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for
solving new path planning problems in high dimensional continuous state and
action spaces. Compared to more classical sampling-based methods like RRT, our
approach achieves much better sample efficiency in high-dimensions and can
benefit from prior experience of planning in similar environments. More
specifically, NEXT exploits a novel neural architecture which can learn
promising search directions from problem structures. The learned prior is then
integrated into a UCB-type algorithm to achieve an online balance between
\emph{exploration} and \emph{exploitation} when solving a new problem. We
conduct thorough experiments to show that NEXT accomplishes new planning
problems with more compact search trees and significantly outperforms
state-of-the-art methods on several benchmarks.Comment: 26 pages, 74 figures, ICLR 2020 spotligh
Deeply Informed Neural Sampling for Robot Motion Planning
Sampling-based Motion Planners (SMPs) have become increasingly popular as
they provide collision-free path solutions regardless of obstacle geometry in a
given environment. However, their computational complexity increases
significantly with the dimensionality of the motion planning problem. Adaptive
sampling is one of the ways to speed up SMPs by sampling a particular region of
a configuration space that is more likely to contain an optimal path solution.
Although there are a wide variety of algorithms for adaptive sampling, they
rely on hand-crafted heuristics; furthermore, their performance decreases
significantly in high-dimensional spaces. In this paper, we present a neural
network-based adaptive sampler for motion planning called Deep Sampling-based
Motion Planner (DeepSMP). DeepSMP generates samples for SMPs and enhances their
overall speed significantly while exhibiting efficient scalability to
higher-dimensional problems. DeepSMP's neural architecture comprises of a
Contractive AutoEncoder which encodes given workspaces directly from a raw
point cloud data, and a Dropout-based stochastic deep feedforward neural
network which takes the workspace encoding, start and goal configuration, and
iteratively generates feasible samples for SMPs to compute end-to-end
collision-free optimal paths. DeepSMP is not only consistently computationally
efficient in all tested environments but has also shown remarkable
generalization to completely unseen environments. We evaluate DeepSMP on
multiple planning problems including planning of a point-mass robot,
rigid-body, 6-link robotic manipulator in various 2D and 3D environments. The
results show that on average our method is at least 7 times faster in
point-mass and rigid-body case and about 28 times faster in 6-link robot case
than the existing state-of-the-art.Comment: 2018 IEEE/RSJ International Conference on Intelligent Robots and
System
Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation
Fast and efficient path generation is critical for robots operating in
complex environments. This motion planning problem is often performed in a
robot's actuation or configuration space, where popular pathfinding methods
such as A*, RRT*, get exponentially more computationally expensive to execute
as the dimensionality increases or the spaces become more cluttered and
complex. On the other hand, if one were to save the entire set of paths
connecting all pair of locations in the configuration space a priori, one would
run out of memory very quickly. In this work, we introduce a novel way of
producing fast and optimal motion plans for static environments by using a
stepping neural network approach, called OracleNet. OracleNet uses Recurrent
Neural Networks to determine end-to-end trajectories in an iterative manner
that implicitly generates optimal motion plans with minimal loss in performance
in a compact form. The algorithm is straightforward in implementation while
consistently generating near-optimal paths in a single, iterative, end-to-end
roll-out. In practice, OracleNet generally has fixed-time execution regardless
of the configuration space complexity while outperforming popular pathfinding
algorithms in complex environments and higher dimension
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
Personal robots assisting humans must perform complex manipulation tasks that
are typically difficult to specify in traditional motion planning pipelines,
where multiple objectives must be met and the high-level context be taken into
consideration. Learning from demonstration (LfD) provides a promising way to
learn these kind of complex manipulation skills even from non-technical users.
However, it is challenging for existing LfD methods to efficiently learn skills
that can generalize to task specifications that are not covered by
demonstrations. In this paper, we introduce a state transition model (STM) that
generates joint-space trajectories by imitating motions from expert behavior.
Given a few demonstrations, we show in real robot experiments that the learned
STM can quickly generalize to unseen tasks and synthesize motions having longer
time horizons than the expert trajectories. Compared to conventional motion
planners, our approach enables the robot to accomplish complex behaviors from
high-level instructions without laborious hand-engineering of planning
objectives, while being able to adapt to changing goals during the skill
execution. In conjunction with a trajectory optimizer, our STM can construct a
high-quality skeleton of a trajectory that can be further improved in
smoothness and precision. In combination with a learned inverse dynamics model,
we additionally present results where the STM is used as a high-level planner.
A video of our experiments is available at https://youtu.be/85DX9Ojq-90Comment: Submitted to IROS 201
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