Presenting our #ICML2024 Position paper: “Automatic Environment Shaping is the Next Frontier in RL”. We argue that more reliable and principled techniques for RL environment shaping will pave the path towards generalist robots 🧵 [1/n] Gabe Margolis Oral talk happening today

Presenting Unsupervised Actuator Nets (UANs) that push the limits of agile whole-body control without the need for reward shaping! ⚡️ UANs reduce the sim2real gap in robot's motors removing the need for reward design to bridge the sim2real gap. ⚡️ A pre-trained whole-body

You aren't doing robotics if you are not breaking some robots! uan.csail.mit.edu

The next frontier for AI shouldn’t just be generally helpful. It should be helpful for you! Our new paper shows how to personalize LLMs — efficiently, scalably, and without retraining. Meet PReF (arxiv.org/abs/2503.06358) 1\n

Catch Nolan Fey's oral/poster at today's ICLR Robot Learning Workshop! robot-learning.ml/2025/ The paper will also appear at RSS.

What’s keeping robot arms from working like human arms? They're big, slow, have the wrong joints, and can't conform to their environment. DexWrist solves all of these issues and simplifies learning constrained, dynamic manipulation👉 dexwrist.csail.mit.edu