Recent interesting robotics papers
NavRL: Learning Safe Flight in Dynamic Environments

- good reward design
- randomized collision environment in IsaacSim and combination of perception in actor-critic network

#RAL2025 #navigation #RL

GitHub Link, Paper Link
GitHub
GitHub - Zhefan-Xu/NavRL: [IEEE RA-L'25] NavRL: Learning Safe Flight in Dynamic Environments (NVIDIA Isaac/Python/ROS1/ROS2)
GitHub - Zhefan-Xu/NavRL: [IEEE RA-L'25] NavRL: Learning Safe Flight in Dynamic Environments (NVIDIA Isaac/Python/ROS1/ROS2)
[IEEE RA-L'25] NavRL: Learning Safe Flight in Dynamic Environments (NVIDIA Isaac/Python/ROS1/ROS2) - Zhefan-Xu/NavRL
RAL2025 navigation RL
Rapidly Adapting Policies to the Real-World via Simulation-Guided Fine-Tuning

- efficient RL algorithm to improve rollout performance

#ICLR2025 #RL #sim2real #refinement

X Overview, Project Website
🧵 Thread • FixupX
Patrick Yin (@patrickhyin)
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…
ICLR2025 RL sim2real refinement
Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model

- using RL with residual policy to fine-tune online policy
- similar to TRANSIC

#ICLR2025 #RL #refinement

X Overview, Project Website, TRANSIC
🧵 Thread • FixupX
Tongzhou Mu 🤖 @ ICLR 2025 (@tongzhou_mu)
🤔 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…
ICLR2025 RL refinement
 
 
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