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Short Bio
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Hi I am Enes! I am currently a Founding
Research Engineer at a
Stealth Robotics Startup. I am interested in robot learning and
how to engineer the best systems for enabling robots to learn better.
I also like to work on high performance software.
Previously I was a Graduate Research
Assistant at CILVR Lab, NYU, where I
worked on mobile manipulation and
tactile sensing, advised by Prof.
Lerrel Pinto.
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Education
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Experience
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Graduate Research Assistant
CILVR, New York University
| New York, USA
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Jan 2022 - Sept 2023
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Worked on problems in Robot Learning,
Specifically in Mobile Manipulation,
Imitation Learning and Tactile Sensing.
Advised by Prof.
Lerrel Pinto
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Research Scholar
EPFL VITA Lab
| Lausanne, Switzerland
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Jul 2022 - Oct 2022
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Research on "World modeling and
hierarchical planning for autonomous
driving"
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Machine Learning Engineer
Wayve
| London, UK
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Jul 2022 - Oct 2022
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I structured the Simulation-as-a-service
for the in-house simulator to be
scalable up to thousands of GPUs. I also
implemented novel view synthesis
simulator for closed-loop offline
evaluation.
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Research &
Publications
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YOR: Your Own Mobile Manipulator for Generalizable Robotics
Manan H Anjaria*, Enes Erciyes*, Vedant Ghatnekar, Neha Navarkar, Haritheja Etukuru, Xiaole Jiang, Kanad Patel, Dhawal Kabra, Nicholas Wojno, Radhika Ajay Prayage, Soumith Chintala, Lerrel Pinto, Nur Muhammad Mahi Shafiullah, Zichen Jeff Cui
project page
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code
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arXiv
Recent advances in robot learning have generated significant interest in capable platforms that may eventually approach human-level competence.
This interest, combined with the commoditization of actuators, has propelled growth in low-cost robotic platforms. However, the optimal form factor for mobile manipulation, especially on a budget, remains an open question. We introduce YOR, an open-source, low-cost mobile manipulator that integrates an omnidirectional base, a telescopic vertical lift, and two arms with grippers to achieve whole-body mobility and manipulation.
Our design emphasizes modularity, ease of assembly using off-the-shelf components, and affordability, with a bill-of-materials cost under 10,000 USD.
We demonstrate YOR's capability by completing tasks that require coordinated whole-body control, bimanual manipulation, and autonomous navigation.
Overall, YOR offers competitive functionality for mobile manipulation research at a fraction of the cost of existing platforms.
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AnySkin: Plug-and-play Tactile
Skins for Robotic
Touch
Raunaq Bhirangi, Venkatesh Pattabiraman,
Enes Erciyes, Yifeng
Cao, Tess Hellebrekers, Lerrel Pinto
ICRA 2025,
Best Paper: RSS 2025 Workshop on
Hardware Intelligence
project page
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code
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arXiv
In this work, we address the
critical challenges that impede the
use of tactile sensing --
versatility, replaceability, and
data reusability. We made a tactile
sensor, AnySkin, that simplifies
integration making it as
straightforward as putting on a
phone case and connecting a charger.
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Template From Here
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