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Eadom Dessalene

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2 papers

1 author row

ICLR Conference 2023 Conference Paper

Mid-Vision Feedback

Michael Maynord
Eadom Dessalene
Cornelia Fermüller
Yiannis Aloimonos

Feedback plays a prominent role in biological vision, where perception is modulated based on agents' evolving expectations and world model. We introduce a novel mechanism which modulates perception based on high level categorical expectations: Mid-Vision Feedback (MVF). MVF associates high level contexts with linear transformations. When a context is "expected" its associated linear transformation is applied over feature vectors in a mid level of a network. The result is that mid-level network representations are biased towards conformance with high level expectations, improving overall accuracy and contextual consistency. Additionally, during training mid-level feature vectors are biased through introduction of a loss term which increases the distance between feature vectors associated with different contexts. MVF is agnostic as to the source of contextual expectations, and can serve as a mechanism for top down integration of symbolic systems with deep vision architectures. We show the superior performance of MVF to post-hoc filtering for incorporation of contextual knowledge, and show superior performance of configurations using predicted context (when no context is known a priori) over configurations with no context awareness.

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ICRA Conference 2019 Conference Paper

Using Geometric Features to Represent Near-Contact Behavior in Robotic Grasping

Eadom Dessalene
Yi Herng Ong
John Morrow
Ravi Balasubramanian
Cindy M. Grimm

In this paper we define two feature representations for grasping. These representations capture hand-object geometric relationships at the near-contact stage - before the fingers close around the object. Their benefits are: 1) They are stable under noise in both joint and pose variation. 2) They are largely hand and object agnostic, enabling direct comparison across different hand morphologies. 3) Their format makes them suitable for direct application of machine learning techniques developed for images. We validate the representations by: 1) Demonstrating that they can accurately predict the distribution of ε-metric values generated by kinematic noise. I. e. , they capture much of the information inherent in contact points and force vectors without the corresponding instabilities. 2) Training a binary grasp success classifier on a real-world data set consisting of 588 grasps.

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