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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Workspace Aware Online Grasp Planning
This work provides a framework for a workspace aware online grasp planner. This framework greatly improves the performance of standard online grasp planning algorithms by incorporating a notion of reachability into the online grasp planning process. Offline, a database of hundreds of thousands of unique end-effector poses were queried for feasibility. At runtime, our grasp planner uses this database to bias the hand towards reachable end-effector configurations. The bias keeps the grasp planner in accessible regions of the planning scene so that the resulting grasps are tailored to the situation at hand. This results in a higher percentage of reachable grasps, a higher percentage of successful grasp executions, and a reduced planning time. We also present experimental results using simulated and real environments.
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- Award ID(s):
- 1734557
- NSF-PAR ID:
- 10111002
- Date Published:
- Journal Name:
- Proceedings Article published Oct 2018 in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Page Range / eLocation ID:
- 2917 to 2924
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IROS.2018.8593644
