Zechu Li

Unlike humans who favor a dominant hand for fine dexterous skills, robots should execute ambidextrous manipulation with equal proficiency. Introducing SYMDEX, our algorithm for Ambidextrous Bimanual Manipulation leveraging robot’s inherent bilateral symmetry as an inductive bias.

🚀 Hiring PhDs & Postdocs in Structured Robot Learning & Embodied AI @TUDarmstadt (PEARL Lab) 🤖 We study how structure in the robot–environment system can be exploited to learn robust, adaptive, and generalizable behaviors, beyond black-box policies 🔬 Topics: • Grounding (language → perception → action) • Structured world models • VLA + control + memory • RL (credit assignment, offline→online) • Whole-body & bimanual mobile manipulation 🇪🇺 New EU Lighthouse Project on Generative AI for Robotics + ERC StG SIREN 👉 Full call: tinyurl.com/pearlgenai Please repost 🙏 #RobotLearning #EmbodiedAI #Robotics #MachineLearning #PhDPositions #Postdoc

@OrdonezApraez Thanks Daniel! It’s an honor to work with you!

Check out our new work on learning ambidexterous bi-manual (and multi-arm) manipulation via morphological symmetry exploitation with Equivariant NNs. Kudos to @softraeh for leading this work.

🧩Web: supersglzc.github.io/projects/symde…, 📷Paper: arxiv.org/abs/2505.05287. It is a pleasure to work with my amazing collaborators Yufeng Jin, @OrdonezApraez and supervisors @GeorgiaChal, @liu_puze, and Claudio Semini.

Notably, our algorithm naturally scales to multi-arm setup with more complicated symmetry groups!

Unlike humans who favor a dominant hand for fine dexterous skills, robots should execute ambidextrous manipulation with equal proficiency. Introducing SYMDEX, our algorithm for Ambidextrous Bimanual Manipulation leveraging robot’s inherent bilateral symmetry as an inductive bias.

Our method significantly outperforms other diffusion-based methods on high-dimensional control benchmarks, and is also competitive with state-of-the-art non-diffusion based RL methods while requiring fewer algorithmic design choices and smaller update-to-data ratios!

Meet our first general-purpose robot at @DexmateAI dexmate.ai/vega Adjustable height from 0.66m to 2.2m: compact enough for an SUV, tall enough to reach those impossible high shelves. Powerful dual arms (15lbs payload each) and omni-directional mobility for ultimate versatility. More videos showcasing Vega in action coming this week! Stay tuned to see this robot partner.

Grateful to have worked with my awesome collaborators Rickmer Krohn, @taochenshh, @aajay3110, and supervisors @GeorgiaChal, @pulkitology!

We design a series of new challenging robot navigation and manipulation tasks with a high degree of multimodality, serving as a testbed for multimodal policy learning. (5/5) 🧩Web: supersglzc.github.io/projects/ddiff…. 📔Paper: arxiv.org/abs/2406.00681.

How can an RL agent successfully solve a task while showcasing versatility of behaviors—a property intuitive to intelligent systems like humans? Introducing Deep Diffusion Policy Gradient (DDiffPG) - our new algorithm for learning multimodal behaviors from scratch! (1/5)

Robot learning in the real world can be expensive and unsafe in human-centric environments. Solution: Construct simulation on the fly and train in it! Excited to share RialTo, led by @marceltornev on learning resilient policies via real-to-sim-to-real policy learning! A 🧵 (1/12)

Working with massive parallel simulation like Isaac Gym? Wonder how to learn policies faster and better than PPO? Check out our poster #102 (1:30pm on Thur) #ICML2023 . We have also open sourced the code. 📔Paper: arxiv.org/abs/2307.12983 🧑‍💻Code: github.com/Improbable-AI/…

@meidusha91 @lidangzzz @shipin_xiazai

【比赛开始】

Introducing Decision Diffuser, a conditional diffusion model that outperforms offline RL across standard benchmarks – using only generative modeling training! Decision Diffusers can also combine multiple constraints and skills at test-time. Website: anuragajay.github.io/decision-diffu… 1/5

“ElegantRL: Much Much More Stable than Stable-Baseline3” by Xiao-Yang Liu link.medium.com/VJAGmZUH7nb