Current RL finetuning methods are too inefficient to make autonomous real world robot learning tractable. We propose Simulation-Guided Fine-Tuning (SGFT) - a simple, general sim2real framework that extracts structured exploration priors from sim to accelerate…

🎯Can robots do better on planning involving neural dynamics?

Introducing our recent work, BaB-ND: https://robopil.github.io/bab-nd/.

Existing sampling- and gradient-based methods may fail short on complex planning problems over non-convex neural dynamics…

🤔 How to fine-tune an Imitation Learning policy (e.g., Diffusion Policy, ACT) with RL? As an RL practitioner, I’ve been struggling with this problem for a while. Here’s why it’s tough:

1️⃣ Special designs (usually for multimodal action distributions) in…

Dive into this #ICLR2025 robotics research paper from #NVIDIAResearch, which focuses on skill retrieval and adaptation for #robotic assembly tasks.

The framework teaches robots new tasks using preexisting skill libraries, boosting zero-shot success rates…