NeurIPS paper adds probabilistic uncertainty quantification to robot memory for better retrieval
lucacarlone1 · x · 2026-10-07
Author lucacarlone1 announces that "Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees" has been accepted at NeurIPS.
The paper adds uncertainty quantification to VLMs and robot memories, using confidence estimates to improve memory retrieval while retaining online performance. Probabilistic guarantees on which memories to trust are relevant for long-running embodied agents.
More from Embodied
- COSMI composes single-object captures into 222k multi-object interaction sequences, 30x larger than prior sets — UniTuebingen · 2026-10-07
- 'Young inventor' AI glasses exposed as 189 yuan Alibaba white-label resell — found by Claude — lxfater · 2026-10-07
- Sesame announces AI eyewear line, Made in Japan and launching in 2027 — testingcatalog · 2026-10-07
- Tesla's Cybercab rests on aggressive FMVSS interpretation NHTSA could reject — binarybits · 2026-10-07
- Waymo Colors Inside FMVSS Lines; Tesla Bets on Regulatory Favor, Analyst Argues — binarybits · 2026-10-07
- 19-Year-Old Founder Zain Raises $11M Seed Led by a16z to Ship Personal AI Computer Core — nick_linck · 2026-10-07