Predicting Sample Collision with Neural Networks
- URL: http://arxiv.org/abs/2006.16868v1
- Date: Tue, 30 Jun 2020 14:56:14 GMT
- Title: Predicting Sample Collision with Neural Networks
- Authors: Tuan Tran, Jory Denny, Chinwe Ekenna
- Abstract summary: We present a framework to address the cost of expensive primitive operations in sampling-based motion planning.
We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces.
- Score: 5.713670854553366
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many state-of-art robotics applications require fast and efficient motion
planning algorithms. Existing motion planning methods become less effective as
the dimensionality of the robot and its workspace increases, especially the
computational cost of collision detection routines. In this work, we present a
framework to address the cost of expensive primitive operations in
sampling-based motion planning. This framework determines the validity of a
sample robot configuration through a novel combination of a Contractive
AutoEncoder (CAE), which captures a occupancy grids representation of the
robot's workspace, and a Multilayer Perceptron, which efficiently predicts the
collision state of the robot from the CAE and the robot's configuration. We
evaluate our framework on multiple planning problems with a variety of robots
in 2D and 3D workspaces. The results show that (1) the framework is
computationally efficient in all investigated problems, and (2) the framework
generalizes well to new workspaces.
Related papers
- Neural Implicit Swept Volume Models for Fast Collision Detection [0.0]
We present an algorithm combining the speed of the deep learning-based signed distance computations with the strong accuracy guarantees of geometric collision checkers.
We validate our approach in simulated and real-world robotic experiments, and demonstrate that it is able to speed up a commercial bin picking application.
arXiv Detail & Related papers (2024-02-23T12:06:48Z) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - Contribution \`a l'Optimisation d'un Comportement Collectif pour un
Groupe de Robots Autonomes [0.0]
This thesis studies the domain of collective robotics, and more particularly the optimization problems of multirobot systems.
The first contribution is the use of the Butterfly Algorithm Optimization (BOA) to solve the Unknown Area Exploration problem.
The second contribution is the development of a new simulation framework for benchmarking dynamic incremental problems in robotics.
arXiv Detail & Related papers (2023-06-10T21:49:08Z) - Simultaneous Contact-Rich Grasping and Locomotion via Distributed
Optimization Enabling Free-Climbing for Multi-Limbed Robots [60.06216976204385]
We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems.
We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
arXiv Detail & Related papers (2022-07-04T13:52:10Z) - Intelligent Trajectory Design for RIS-NOMA aided Multi-robot
Communications [59.34642007625687]
The goal is to maximize the sum-rate of whole trajectories for multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots.
An integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$3$QN) algorithm.
arXiv Detail & Related papers (2022-05-03T17:14:47Z) - Show Me What You Can Do: Capability Calibration on Reachable Workspace
for Human-Robot Collaboration [83.4081612443128]
We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth.
We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
arXiv Detail & Related papers (2021-03-06T09:14:30Z) - Large Scale Distributed Collaborative Unlabeled Motion Planning with
Graph Policy Gradients [122.85280150421175]
We present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots.
We employ a graph neural network (GNN) to parameterize policies for the robots.
arXiv Detail & Related papers (2021-02-11T21:57:43Z) - An advantage actor-critic algorithm for robotic motion planning in dense
and dynamic scenarios [0.8594140167290099]
In this paper, we modify existing advantage actor-critic algorithm and suit it to complex motion planning.
It achieves higher success rate in motion planning with lesser processing time for robot to reach its goal.
arXiv Detail & Related papers (2021-02-05T12:30:23Z) - Towards Coordinated Robot Motions: End-to-End Learning of Motion
Policies on Transform Trees [63.31965375413414]
We propose to solve multi-task problems through learning structured policies from human demonstrations.
Our structured policy is inspired by RMPflow, a framework for combining subtask policies on different spaces.
We derive an end-to-end learning objective function that is suitable for the multi-task problem.
arXiv Detail & Related papers (2020-12-24T22:46:22Z) - Fast-reactive probabilistic motion planning for high-dimensional robots [15.082715993594121]
p-Chekov is a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises.
Comprehensive theoretical and empirical analysis shows that p-Chekov can effectively satisfy user-specified chance constraints over collision risk in practical robotic manipulation tasks.
arXiv Detail & Related papers (2020-12-03T17:51:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.