293 research outputs found
Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter
Camera viewpoint selection is an important aspect of visual grasp detection,
especially in clutter where many occlusions are present. Where other approaches
use a static camera position or fixed data collection routines, our Multi-View
Picking (MVP) controller uses an active perception approach to choose
informative viewpoints based directly on a distribution of grasp pose estimates
in real time, reducing uncertainty in the grasp poses caused by clutter and
occlusions. In trials of grasping 20 objects from clutter, our MVP controller
achieves 80% grasp success, outperforming a single-viewpoint grasp detector by
12%. We also show that our approach is both more accurate and more efficient
than approaches which consider multiple fixed viewpoints.Comment: ICRA 2019 Video: https://youtu.be/Vn3vSPKlaEk Code:
https://github.com/dougsm/mvp_gras
Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking
A new generation of automated bin picking systems using deep learning is
evolving to support increasing demand for e-commerce. To accommodate a wide
variety of products, many automated systems include multiple gripper types
and/or tool changers. However, for some objects, sequential grasp failures are
common: when a computed grasp fails to lift and remove the object, the bin is
often left unchanged; as the sensor input is consistent, the system retries the
same grasp over and over, resulting in a significant reduction in mean
successful picks per hour (MPPH). Based on an empirical study of sequential
failures, we characterize a class of "sequential failure objects" (SFOs) --
objects prone to sequential failures based on a novel taxonomy. We then propose
three non-Markov picking policies that incorporate memory of past failures to
modify subsequent actions. Simulation experiments on SFO models and the EGAD
dataset suggest that the non-Markov policies significantly outperform the
Markov policy in terms of the sequential failure rate and MPPH. In physical
experiments on 50 heaps of 12 SFOs the most effective Non-Markov policy
increased MPPH over the Dex-Net Markov policy by 107%.Comment: 2020 IEEE International Conference on Automation Science and
Engineering (CASE
Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning
Enabling autonomous robots to interact in unstructured environments with
dynamic objects requires manipulation capabilities that can deal with clutter,
changes, and objects' variability. This paper presents a comparison of
different reinforcement learning-based approaches for object picking with a
robotic manipulator. We learn closed-loop policies mapping depth camera inputs
to motion commands and compare different approaches to keep the problem
tractable, including reward shaping, curriculum learning and using a policy
pre-trained on a task with a reduced action set to warm-start the full problem.
For efficient and more flexible data collection, we train in simulation and
transfer the policies to a real robot. We show that using curriculum learning,
policies learned with a sparse reward formulation can be trained at similar
rates as with a shaped reward. These policies result in success rates
comparable to the policy initialized on the simplified task. We could
successfully transfer these policies to the real robot with only minor
modifications of the depth image filtering. We found that using a heuristic to
warm-start the training was useful to enforce desired behavior, while the
policies trained from scratch using a curriculum learned better to cope with
unseen scenarios where objects are removed.Comment: 8 pages, video available at https://youtu.be/ii16Zejmf-
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
When operating in unstructured environments such as warehouses, homes, and
retail centers, robots are frequently required to interactively search for and
retrieve specific objects from cluttered bins, shelves, or tables. Mechanical
Search describes the class of tasks where the goal is to locate and extract a
known target object. In this paper, we formalize Mechanical Search and study a
version where distractor objects are heaped over the target object in a bin.
The robot uses an RGBD perception system and control policies to iteratively
select, parameterize, and perform one of 3 actions -- push, suction, grasp --
until the target object is extracted, or either a time limit is exceeded, or no
high confidence push or grasp is available. We present a study of 5 algorithmic
policies for mechanical search, with 15,000 simulated trials and 300 physical
trials for heaps ranging from 10 to 20 objects. Results suggest that success
can be achieved in this long-horizon task with algorithmic policies in over 95%
of instances and that the number of actions required scales approximately
linearly with the size of the heap. Code and supplementary material can be
found at http://ai.stanford.edu/mech-search .Comment: To appear in IEEE International Conference on Robotics and Automation
(ICRA), 2019. 9 pages with 4 figure
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