NeuralActuator Wins RSS System Award and Goes Open Source
MIT_CSAIL · x · 2026-07-16
NeuralActuator has won the RSS 2026 Outstanding Systems Paper Award.
The author announced that the project is now fully open-sourced, including the code, pre-trained checkpoints, datasets, data collection pipeline, and hardware setup instructions. The repository also contains additional experimental and technical details not included in the paper, such as residual torque modeling and different approaches to implicitly/explicitly coupling external force estimation with differentiable simulation.
The team is continuously collecting actuator data across different platforms and welcomes data contributions and collaborations.
Related event: MIT and Amazon Introduce NeuralActuator for Sensorless Force Sensing(3 posts)→
More from Embodied
- ECCV26 Oral: Flow Matching Enables Single-Stage Multi-View Point Cloud Registration — ducha_aiki · 2026-09-11
- Polish developers build iPhone app that detects nearby Meta smart glasses — Low-Honeydew6483 · 2026-09-11
- Ant's Afu health AI hits 150M users, unveils AI+hardware health alliance at Bund Summit — APPSO · 2026-09-11
- Johns Hopkins Launches Full-Stack Hands-on Robot Learning Class with SO-101 Arm Kits — _krishna_murthy · 2026-09-11
- SyncWorld: In-Context Robot World Model Simulates Unseen Views and Embodiments Zero-Shot — ChongZzZhang · 2026-09-11
- A 3D Pose Dataset for Dogs Released — ducha_aiki · 2026-09-11