https://nitter.cf/t.co/uvo8cqvX8B | Ph.D student @GeorgiaTech | Prev @Kimi_Moonshot Foundation models / agentic systems for robotics
Joined February 2024
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🚀 Frontier models are becoming increasingly capable in robotics!
RLE-Bench has also been accepted to the CoRL Workshop on Agentic Robotics! Would be fun to chat if you are interested in benchmarks/harnesses/agent training/new opportunities for foundation models in robotics.
agentic-robotics-workshop.gi…
🚀 Gemini 4 Argon takes #1 on 4 robotics tasks in RLE-Bench!
The latest Gemini 4 Argon results are in: 72.4 RLE Index, ranking #2 overall, just behind GPT-6 Astra (73.4).
More impressively, Gemini 4 Argon takes the top spot on 4 out of 9 tasks:
* Harness Engineering
* Embodied Reasoning
* Whole-Body Motion Tracking
* NanoVLA Recipe
It’s exciting to see frontier AI labs continuing to invest in robotics capabilities, with each new generation pushing the boundaries of what general-purpose AI agents can accomplish in embodied and physical-world tasks.
The competition is getting closer, and the progress is accelerating.
Looking forward to the Carbon! 👀
Explore the full leaderboard: rle-bench.github.io
#Robotics #EmbodiedAI #RLEBench #Gemini4
Great work by @rqiang_rushi! My biggest takeaway: keep the harness thin and let agents discover what matters through solving tasks. Instead of predefining skills like move and grasp, our harness provides basic device access and lets agents build their own. We’ve released the full library of skills and tools in the blog, and it’s fun to see what emerges!
Coding agents can solve all RoboCasa tasks from scratch, by self-evolving its skills and tools from its own trials.
Our system grew its library from empty to 6 canonical skills and 70 functions in seven rounds, and solved all 365 tasks.
See details: stacx-lab.github.io/physical…
Chenxiao Gao retweeted
Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model.
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Open weights by Nov 4, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro
Introducing Kinex, the harness that connects coding agents to any physical robot, unlocking scalable learning in the physical world.
Tell Kinex about your robot; it then carries out tasks and learns from every run. We demonstrate Kinex across diverse real-world applications in a zero-shot setting, from bimanual manipulation to quadruped robots.
🧵1/5
More details and demos on our project website:
stacx-lab.github.io/kinex/
Try it out and see how coding agents can control real robots 🤖
Kinex decomposes a robot into a collection of devices, and the agent interacts with devices through device drivers, tools, and under the guidance of skills.
🧵2/5
Try Kinex in simulation or on your own robot.
Install → run Kinex → connect a model → set up your devices → describe the task.
🧵4/5
Kinex is built on the Pi harness.
Plugins also bring Kinex to Pi, Codex, and Claude Code.
Check out the docs, demos, and code here: stacx-lab.github.io/kinex/
🧵5/5
New on the RLE-Bench leaderboard 🚀
Frontier models keep getting better at robotics, and RLE-Bench gives us a measurable way to track it.
Two updates on RLE-Bench! 🤖
📈 New results for GPT-6.1-Sol, Opus 5.5, Fable 5.1, GPT-6-Luna, and Grok 4.6! Opus 5.5 shows substantial gains in interactive control—even outperforming Fable 5.1. Exciting to see frontier models making progress on robotics tasks!
📑 Our technical report is out! We dive into the design principles behind RLE-Bench, break down each task, and share observations of how agents tackle them. Plenty of interesting agent behaviors to explore!
Check out the latest results and read the report 👇
Excited to share RLE-Bench, a benchmark we’ve been building over the past few months. We envision agents that can perceive, control robots, develop policies, and design their own hardware. RLE-Bench measures how close today’s coding agents are to that vision.