468 research outputs found
Learning to Singulate Objects using a Push Proposal Network
Learning to act in unstructured environments, such as cluttered piles of
objects, poses a substantial challenge for manipulation robots. We present a
novel neural network-based approach that separates unknown objects in clutter
by selecting favourable push actions. Our network is trained from data
collected through autonomous interaction of a PR2 robot with randomly organized
tabletop scenes. The model is designed to propose meaningful push actions based
on over-segmented RGB-D images. We evaluate our approach by singulating up to 8
unknown objects in clutter. We demonstrate that our method enables the robot to
perform the task with a high success rate and a low number of required push
actions. Our results based on real-world experiments show that our network is
able to generalize to novel objects of various sizes and shapes, as well as to
arbitrary object configurations. Videos of our experiments can be viewed at
http://robotpush.cs.uni-freiburg.deComment: International Symposium on Robotics Research (ISRR) 2017, videos:
http://robotpush.cs.uni-freiburg.d
Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting
We present an interactive perception model for
object sorting based on Gaussian Process (GP) classification
that is capable of recognizing objects categories from point
cloud data. In our approach, FPFH features are extracted from
point clouds to describe the local 3D shape of objects and
a Bag-of-Words coding method is used to obtain an object-level
vocabulary representation. Multi-class Gaussian Process
classification is employed to provide and probable estimation of
the identity of the object and serves a key role in the interactive
perception cycle – modelling perception confidence. We show
results from simulated input data on both SVM and GP based
multi-class classifiers to validate the recognition accuracy of our
proposed perception model. Our results demonstrate that by
using a GP-based classifier, we obtain true positive classification
rates of up to 80%. Our semi-autonomous object sorting
experiments show that the proposed GP based interactive
sorting approach outperforms random sorting by up to 30%
when applied to scenes comprising configurations of household
objects
Interactive singulation of objects from a pile
Abstract—Interaction with unstructured groups of objects allows a robot to discover and manipulate novel items in cluttered environments. We present a framework for interactive singulation of individual items from a pile. The proposed framework provides an overall approach for tasks involving operation on multiple objects, such as counting, arranging, or sorting items in a pile. A perception module combined with pushing actions accumulates evidence of singulated items over multiple pile interactions. A decision module scores the likelihood of a single-item pile to a multiple-item pile based on the magnitude of motion and matching determined from the perception module. Three variations of the singulation framework were evaluated on a physical robot for an arrangement task. The proposed interactive singulation method with adaptive pushing reduces the grasp errors on non-singulated piles compared to alternative methods without the perception and decision modules. This work contributes the general pile interaction framework, a specific method for integrating perception and action plans with grasp decisions, and an experimental evaluation of the cost trade-offs for different singulation methods. I
Hierarchical Policy Learning for Mechanical Search
Retrieving objects from clutters is a complex task, which requires multiple
interactions with the environment until the target object can be extracted.
These interactions involve executing action primitives like grasping or pushing
as well as setting priorities for the objects to manipulate and the actions to
execute. Mechanical Search (MS) is a framework for object retrieval, which uses
a heuristic algorithm for pushing and rule-based algorithms for high-level
planning. While rule-based policies profit from human intuition in how they
work, they usually perform sub-optimally in many cases. Deep reinforcement
learning (RL) has shown great performance in complex tasks such as taking
decisions through evaluating pixels, which makes it suitable for training
policies in the context of object-retrieval. In this work, we first formulate
the MS problem in a principled formulation as a hierarchical POMDP. Based on
this formulation, we propose a hierarchical policy learning approach for the MS
problem. For demonstration, we present two main parameterized sub-policies: a
push policy and an action selection policy. When integrated into the
hierarchical POMDP's policy, our proposed sub-policies increase the success
rate of retrieving the target object from less than 32% to nearly 80%, while
reducing the computation time for push actions from multiple seconds to less
than 10 milliseconds.Comment: ICRA 202
Efficient Object Isolation In Complex Environment Using Manipulation Primitive On A Vision Based Mobile 6DOF Robotic Arm
This paper explores the idea of manipulation aided- perception in the context of isolating an object of
interest from other small objects of varying degree of
clusterization in order to obtain high quality training
images.The robot utilizes a novel algorithm to plot out the position for each noise objects and its destined position as well as its trajectory and then utilizes manipulation primitives (pushing motion) to move said object along the planned trajectory.The method was demonstrated using Vrep simulation software which simulated a Kuka YouBot fitted with a camera on the gripper.We evaluated our approach by simulating the robot manipulators in an experiment which successfully isolate the object of interest from noise objects with at a rate of 77.46% at an average of 0.56 manipulations per object compared to others at 1.76 manipulations subsequently speeding up the time taken for manipulation from 12.58 minutes to 2.6 minutes however suffers from a tradeoff in terms of accuracy when comparing the similar works to our proposed method
Persistent Homology Guided Monte-Carlo Tree Search for Effective Non-Prehensile Manipulation
Performing object retrieval tasks in messy real-world workspaces involves the
challenges of \emph{uncertainty} and \emph{clutter}. One option is to solve
retrieval problems via a sequence of prehensile pick-n-place operations, which
can be computationally expensive to compute in highly-cluttered scenarios and
also inefficient to execute. The proposed framework selects the option of
performing non-prehensile actions, such as pushing, to clean a cluttered
workspace to allow a robotic arm to retrieve a target object. Non-prehensile
actions, allow to interact simultaneously with multiple objects, which can
speed up execution. At the same time, they can significantly increase
uncertainty as it is not easy to accurately estimate the outcome of a pushing
operation in clutter. The proposed framework integrates topological tools and
Monte-Carlo tree search to achieve effective and robust pushing for object
retrieval problems. In particular, it proposes using persistent homology to
automatically identify manageable clustering of blocking objects in the
workspace without the need for manually adjusting hyper-parameters.
Furthermore, MCTS uses this information to explore feasible actions to push
groups of objects together, aiming to minimize the number of pushing actions
needed to clear the path to the target object. Real-world experiments using a
Baxter robot, which involves some noise in actuation, show that the proposed
framework achieves a higher success rate in solving retrieval tasks in dense
clutter compared to state-of-the-art alternatives. Moreover, it produces
high-quality solutions with a small number of pushing actions improving the
overall execution time. More critically, it is robust enough that it allows to
plan the sequence of actions offline and then execute them reliably online with
Baxter
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