Expired50% confidenceFactExact time
The Acceleration Consortium at the University of Toronto, the ChemOS project at Carnegie Mellon University, and the mobile robotic chemist developed by Andrew Cooper's team at the University of Liverpool have already demonstrated the value of SDLs in the domains of photocatalyst and porous material optimization.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/17/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/15/2026(expired)
Sources
Related Claims
UnverifiedMIT的Cheetah系列机器人和CMU研究团队通过拓扑优化与强化学习联合训练,可设计出外壳即结构承力件同时最大化关节活动自由度的形态解61% similarUnverified协作机器人的安全协作能力建立在ISO/TS 15066技术规范定义的四种协作模式之上:安全额定监控停止、手动引导、速度与间距监控、以及功率与力限制56% similarUnverifiedTeams at Boston Dynamics, Figure AI, and others have incorporated shell-cooperative kinematics optimization into their standard design workflows.56% similarUnverifiedPhysical Intelligence's π0 (2024) adopts a flow-matching-based action generation head, achieving state-of-the-art performance on fine manipulation tasks such as folding clothes and assembling meal boxes.56% similarUnverifiedResearch Engineer/Applied Scientist偏研究导向看重发表记录,Machine Learning Engineer偏工程导向看重系统设计和生产部署56% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/537349API
curl https://kongchang.com/api/v1/knowledge/claims/537349MCP
get_claim(id=537349)