About

Scott Kuindersma is a robotics researcher whose work spans legged locomotion, trajectory optimization, wearable robotics, and robot learning — fields in which he has made lasting and influential contributions. He is perhaps best known for leading the optimization-based planning and control framework developed for Boston Dynamics' Atlas humanoid robot, a landmark achievement that has garnered over 800 citations and remains a cornerstone reference in humanoid robotics. His interdisciplinary reach extends into human augmentation, with his highly cited work on human-in-the-loop optimization of soft exosuits (615 citations) demonstrating how personalized controllers can meaningfully improve walking efficiency for individual users. Earlier in his career, Kuindersma contributed foundational ideas in robot learning from demonstration, introducing the CST algorithm for constructing hierarchical skill trees (300 citations), and explored autonomous skill acquisition on mobile manipulators. His theoretical contributions include advances in contact-implicit trajectory optimization and constrained dynamic programming, addressing the numerical challenges of planning for robots that interact physically with their environment. Through his involvement in the DARPA Robotics Challenge and his chapter on modeling and control of legged robots, Kuindersma has helped shape both the practical and pedagogical landscape of modern robotics research.

Research Focus

Key Achievements

19
H-Index
28
Papers
2,882
Total Citations
103
Avg Citations/Paper
🏆 Most Cited Paper
Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot
814 citations · 2015
📈 Most Prolific Year: 2018 (4 Papers)
🤝 Key Collaborators: 67
🏛 Institutions: Massachusetts Institute of Technology, Harvard University, Amherst College, Vassar College, University of Massachusetts Amherst, Harvard University Press

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago