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Yiting Chen Profile
Yiting Chen

@YitingChen07

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PhD Student in Robot Manipulation @RiceUniversity

Joined April 2025
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@YitingChen07
Yiting Chen
10 days
“You can’t make progress until you are able to measure it. Robotics still doesn’t have such a rallying call. No one agrees on anything.” I 💯 agree with the recent post from @DrJimFan. To break this impasse, we are excited to announce ManipulationNet ( https://t.co/DvoN2nvURq), a
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@YitingChen07
Yiting Chen
6 days
This is the correct paradigm for benchmarking manipulation and now it arrives! We always welcome new task proposals; our infrastructure can support them all. More details at
@chris_j_paxton
Chris Paxton
6 days
As much as you can do system-level evaluation on the cheap this is the only way i think. - mail out standard object sets - have a client which records task performance and streams it
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@YitingChen07
Yiting Chen
8 days
If your robot can’t tell colors, how can it perceive the world? If it can’t stably stack blocks, how can it understand physics? If it can’t link language to space, how can it reason? If it can’t contextualize tasks, how can it be generalizable? Before claiming “general reasoning
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@YitingChen07
Yiting Chen
10 days
"One infrastructure to host a wide range of real-world benchmarking tasks" Now starts from the classic peg-in-hole assembly and block arrangement, to a general network of manipulation skills and abilities!
@kaiwynd
Kaifeng Zhang
10 days
The pursuit of a unified benchmark for robot manipulation has been long-standing — and with ManipulationNet, it’s finally taking a leap forward! https://t.co/6ZnGzHykj2 ManipulationNet is a fully real-world, verifiable, and scalable benchmark for robotic manipulation. Anyone
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@YitingChen07
Yiting Chen
10 days
A huge thank you to all our committee members: Kaiyu Hang, Kenneth Kimble, Edward H. Adelson, Tamim Asfour, @DanicaKragic, Xiang Li, @YunzhuLiYZ , Aaron Prather, Nancy Pollard, Maximo A. Roa-Garzon, Robert Seney, and @yukez, and our developer team Podshara Chanrungmaneekul,
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@YitingChen07
Yiting Chen
10 days
ManipulationNet is born as a scalable infrastructure to support a wide range of real-world benchmarking tasks! Whether the tasks require interactive commands or are pre-configured for automated execution, they can all be benchmarked at scale within this paradigm. (6/7)
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@YitingChen07
Yiting Chen
10 days
The mnet-server is a central manager for all client connections, which is responsible for handling all task-relevant activities. It supports task instructions delivery, performance logs collection in real-time for distributed benchmarking. In general, the mnet-server is
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@YitingChen07
Yiting Chen
10 days
The mnet-client is a middle layer between the robotic system and the mnet-server (will be introduced in the following post) to support distributed manipulation benchmarking on standardized task setups. The robotic system communicates with the mnet-client through ROS services and
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@YitingChen07
Yiting Chen
10 days
ManipulationNet provides a re-imagined paradigm, which relies on the integrated interaction between distributed hardware (for reproducible task setup in the real world) and software (for performance collection and task instructions delivery) to achieve the goal of distributed
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@YitingChen07
Yiting Chen
10 days
ManipulationNet organizes benchmarking tasks in two tracks: 1) Physical Skills Track that evaluates manipulation skills in physical interaction-rich tasks; and 2) Embodied Reasoning Track that challenges robot capabilities in reasoning and multimodal grounding. (2/7)
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@ieee_ras_icra
IEEE ICRA
5 months
⭐Thank you to everyone who made #ICRA2025 a success! Your participation was paramount to this successful conference, which brought together 7,000 robotics experts and enthusiasts. 📆 Save the date! #ICRA2026 will take place in Vienna, Austria, from 1–5 June 2026.
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@VectorWang2
Vector Wang
6 months
Introduce XLeRobot: https://t.co/9hK0e8uNgC A practical low-cost household open source dual-arm mobile robot for general manipulation! Get yourself one with ~$660 and easily build it in <7hrs! (~$250 and <2hrs with a SO100 and a Lekiwi) Fully based on LeRobot More below:
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