ActiveScale Trains Robots to Actively Perceive Using 1000 Hours of Egocentric Data
zhenjun_zhao · x · 2026-09-17
ActiveScale advances robotic active perception through coordinated model, data, and hardware design, addressing occlusion when fixed viewpoints miss task-relevant information.
- Model: Augments a VLA with historical video observations and explicit camera-pose supervision, using per-frame pose tokens and a lightweight prediction head to associate observations across viewpoints.
- Data: A scalable human–robot mid-training recipe uses 1000 hours of egocentric and robotic data, leveraging natural camera motion in human activity to adapt the model to temporal inputs and pose supervision.
- Hardware: The Active-perception Mobile-manipulation Platform (AMP) supports single-operator teleoperation for scalable demonstration collection.
- Results: Higher success rates on active-perception tasks; ablations confirm the value of pose-aware modeling and egocentric mid-training.
Related event: ActiveScale: Co-Designed Framework Teaches Robots Active Perception(2 posts)→
More from Embodied
- Nano-texture iPhone Duo display makes Apple Pencil feel like writing on paper — rudrank · 2026-11-04
- NEURA launches Neuron AI, but Europe's Physical AI test is whether the stack matures together — ingliguori · 2026-11-04
- Baidu's Apollo Go wraps two-week public robotaxi test rides in Tung Chung, Hong Kong — Baidu_Inc · 2026-11-04
- imec neural probe integrates photonics directly into 130nm CMOS backend — jwt0625 · 2026-11-04
- Chris Paxton: interactive robot sim is the future but glaringly data-limited — Scobleizer · 2026-11-04
- DreamX: video diffusion models as universal latent action learners for robots — kaist-ai · 2026-11-04