[Quantum-ms] [Seminar] EE Faculty Candidate Lecture 3/25 at 11:40am

[Columbia EE Events]

Hi all, just a reminder that the below event will be happening tmrw at
CEPSR 750 at 11:40 am.

On Thu, Mar 13, 2025 at 4:28 PM Columbia EE Events <
ee-events at ee.columbia.edu> wrote:

> [image: Left-aligned blue cuee logo.png]
>
> *Please join us for EE faculty candidate lecture on 3/25!*
>
> *When: *3/25, Tuesday, 11:40am - 12:40pm
> *Where: *CEPSR 750, also via zoom
> <https://columbiauniversity.zoom.us/j/4419920270#success >.
> *Who:* Dr. Haoshu Fang
> *Title: *Data Science for Human-level Robotic Manipulation
> <https://www.ee.columbia.edu/events/ee-seminar-data-science-human-level-robotic-manipulation >
>
> *Abstract:* Machine learning has revolutionized many subfields of
> robotics, from visual perception to task planning. However, the fundamental
> challenge of low-level motor control for object manipulation with raw
> sensory observations remains unresolved, primarily due to the lack of robot
> state and action data during manipulation. This issue is particularly
> pronounced in tasks requiring multi-finger coordination and fine tactile
> sensing. Addressing the data problem is essential, as many modalities of
> robotic data, such as tactile and proprioceptive information, are not
> readily available online. The key scientific questions in this domain are:
> (i) how to collect data, (ii) what data to collect, and (iii) how to learn
> effectively from such data. In this talk, I will (i) introduce a novel
> paradigm for data collection through the design of innovative interaction
> interfaces, (ii) demonstrate how identifying key dimensions for scaling
> data can enable human-level robotic grasping, and (iii) present methods and
> insights on efficiently learning from heterogeneous robotic data.
>
> *Bio: *Haoshu Fang is a Postdoctoral Researcher at MIT CSAIL, working
> with Pulkit Agrawal and Edward Adelson. He earned his PhD from Shanghai
> Jiao Tong University. Haoshu's research focuses on general robotic
> manipulation, addressing the data challenge by designing data-centric
> hardware, leveraging data scaling laws, and developing data-efficient
> learning methods. His work has been recognized with three best paper or
> nomination awards at top robotics conferences and prestigious fellowships
> from Microsoft, Baidu, and ByteDance.
>
> *Faculty Host: *James Anderson
>
>
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