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Mei Hao

dblp:342/2913 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 since 2021Systems, architecture and hardware · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
assembly
0.712023
Towards Generalized Robot Assembly through Compliance-Enabled Contact Formations · ICRA 2023
Robotics › Robot manipulation
contact-rich manipulation
0.712023
Towards Generalized Robot Assembly through Compliance-Enabled Contact Formations · ICRA 2023
Robotics › Robot manipulation › assembly
peg-in-hole insertion
0.712023
Towards Generalized Robot Assembly through Compliance-Enabled Contact Formations · ICRA 2023

Methods — techniques the papers use, named apart from their topics

force monitoring · 0.7compliant control · 0.7
YearPublicationVenuePosition
2026 SpFreNet: A Parallel Spatial-Frequency Dual-Branch Network for Few-Shot Image Classification
Guoliang Gong, Mei Hao, Yu Zhang 0038
ICIC (17)2
2026 A global-local hybrid perception model for few-shot fine-grained image classification
Yu Zhang 0038, Mei Hao, Juan Lyu, Sai-Ho Ling, Guoliang Gong
Pattern Recognit.2
2023 Towards Generalized Robot Assembly through Compliance-Enabled Contact Formations
abstract
Contact can be conceptualized as a set of constraints imposed on two bodies that are interacting with one another in some way. The nature of a contact, whether a point, line, or surface, dictates how these bodies are able to move with respect to one another given a force, and a set of contacts can provide either partial or full constraint on a body's motion. Decades of work have explored how to explicitly estimate the location of a contact and its dynamics, e.g., frictional properties, but investigated methods have been computationally expensive and there often exists significant uncertainty in the final calculation. This has affected further advancements in contact-rich tasks that are seemingly simple to humans, such as generalized peg-in-hole insertions. In this work, instead of explicitly estimating the individual contact dynamics between an object and its hole, we approach this problem by investigating compliance-enabled contact formations. More formally, contact formations are defined according to the constraints imposed on an object's available degrees-of-freedom. Rather than estimating individual contact positions, we abstract out this calculation to an implicit representation, allowing the robot to either acquire, maintain, or release constraints on the object during the insertion process, by monitoring forces enacted on the end effector through time. Using a compliant robot, our method is desirable in that we are able to complete industry-relevant insertion tasks of tolerances <0.25mm without prior knowledge of the exact hole location or its orientation. We showcase our method on more generalized insertion tasks, such as commercially available non-cylindrical objects and open world plug tasks.
Andrew S. Morgan, Quentin Bateux, Mei Hao, Aaron M. Dollar
ICRA3