We present the design, implementation, and evaluation of RF-Grasp, a robotic system that can grasp fully-occluded objects in unknown and unstructured environments. Unlike prior systems that are constrained by the line-of-sight perception of vision and infrared sensors, RF-Grasp employs RF (Radio Frequency) perception to identify and locate target objects through occlusions, and perform efficient exploration and complex manipulation tasks in non-line-of-sight settings.RF-Grasp relies on an eye-in-hand camera and batteryless RFID tags attached to objects of interest. It introduces two main innovations: (1) an RF-visual servoing controller that uses the RFID’s location to selectively explore the environment and plan an efficient trajectory toward an occluded target, and (2) an RF-visual deep reinforcement learning network that can learn and execute efficient, complex policies for decluttering and grasping.We implemented and evaluated an end-to-end physical prototype of RF-Grasp. We demonstrate it improves success rate and efficiency by up to 40-50% over a state-of-the-art baseline. We also demonstrate RF-Grasp in novel tasks such mechanical search of fully-occluded objects behind obstacles, opening up new possibilities for robotic manipulation. Qualitative results (videos) available at rfgrasp.media.mit.edu
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Grasping Fragile Objects Using A Stress-Minimization Metric
We present a new method to generate optimal grasps for brittle and fragile objects using a novel stressminimization (SM) metric. Our approach is designed for objects that are composed of homogeneous isotopic materials. Our SM
metric measures the maximal resistible external wrenches that
would not result in fractures in the target objects. In this paper,
we propose methods to compute our new metric. We also use
our SM metric to design optimal grasp planning algorithms.
Finally, we compare the performance of our metric and conventional grasp metrics, including Q1, Q∞, QG11, QMSV , QV EW .
Our experiments show that our SM metric takes into account
the material characteristics and object shapes to indicate the
fragile regions, where prior methods may not work well. We
also show that the computational cost of our SM metric is on
par with prior methods. Finally, we show that grasp planners
guided by our metric can lower the probability of breaking
target objects.
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- Award ID(s):
- 1910486
- NSF-PAR ID:
- 10190830
- Date Published:
- Journal Name:
- International Conference on Robotics and Automation (ICRA 2020)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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