Grasping in dynamic environments presents a
unique set of challenges. A stable and reachable grasp can
become unreachable and unstable as the target object moves,
motion planning needs to be adaptive and in real time, the
delay in computation makes prediction necessary. In this paper,
we present a dynamic grasping framework that is reachabilityaware
and motion-aware. Specifically, we model the reachability
space of the robot using a signed distance field which enables
us to quickly screen unreachable grasps. Also, we train a
neural network to predict the grasp quality conditioned on the
current motion of the target. Using these as ranking functions,
we quickly filter a large grasp database to a few grasps in
real time. In addition, we present a seeding approach for arm
motion generation that utilizes solution from previous time step.
This quickly generates a new arm trajectory that is close to
the previous plan and prevents fluctuation. We implement a
recurrent neural network (RNN) for modelling and predicting
the object motion. Our extensive experiments demonstrate the
importance of each of these components and we validate our
pipeline on a real robot.
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The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
A robot can now grasp an object more effectively than ever before, but once it has the object what happens next? We show that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances. To address this, we introduce the JHU CoSTAR Block Stacking Dataset (BSD), where a robot interacts with 5.1 cm colored blocks to complete an order-fulfillment style block stacking task. It contains dynamic scenes and real time-series data in a less constrained environment than comparable datasets. There are nearly 12,000 stacking attempts and over 2 million frames of real data. We discuss the ways in which this dataset provides a valuable resource for a broad range of other topics of investigation. We find that hand-designed neural networks that work on prior datasets do not generalize to this task. Thus, to establish a baseline for this dataset, we demonstrate an automated search of neural network based models using a novel multiple-input HyperTree MetaModel, and find a final model which makes reasonable 3D pose predictions for grasping and stacking on our dataset. The CoSTAR BSD, code, and instructions are available at sites.google.com/site/costardataset
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- Award ID(s):
- 1637949
- PAR ID:
- 10136850
- Date Published:
- Journal Name:
- IROS 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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