Feng Chen

@Winniechen02

Ph.D. student. at the University of Hong Kong, advised by prof. Yi Ma Graduated from IIIS Tsinghua University 2024 (Yao Class)

Central & Western District

Pinned
Feng Chen
@Winniechen02
May 19
🤖Can robots play Texas Hold’em? 🤔 We introduce DexHoldem, a real-world benchmark for dexterous manipulation and embodied agents. After fine-tuning leading VLAs and pairing them with frontier agents, we found: Robots can move the chips — sometimes. Agents can read the table
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Feng Chen
@Winniechen02
Sep 10
29hours, GPT6, only vision input and perception state, 38 times trying, rotating the pocket cube one time. Something interesting is GPT6 tried to access more information in simulator but detected by itself and killed.
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Feng Chen
@Winniechen02
Sep 8
The recent attempt in agentic robotics has inspired me. 1. The grasping task usually has a good effect, because the grasping itself is closer to the processing of static scenes (including dexterous hand grasping!) 2. Collisions and avoiding collisions are difficult for LLM, and
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Feng Chen
@Winniechen02
Sep 4
Nice work! Congrats!
Xunzhe Zhou (周训哲)
@XunzheZhou
Aug 31
We’re releasing The Imitator Game, a hierarchical benchmark for Human→Robot video imitation. @GeneralistAI @SkildAI showed that scaling human-video-prompting can enable one-shot robot task learning. We built the benchmark for the next question: How far can robots go beyond
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Feng Chen
@Winniechen02
Jul 30
For dexterous hand, codex could also do something interesting. Agent would take image, robot states as input, and it could use some open source models without specific preparation.
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