37 citations
- Amazon (United States)US1 paper
- Harvard UniversityUS1 paper
- Harvard University PressUS1 paper
- MIT Computer Science and Artificial Intelligence LaboratoryUS1 paper
- Northwestern UniversityUS1 paper
- Shirley Ryan AbilityLabUS1 paper
- University of California, Los AngelesUS1 paper
- University of PennsylvaniaUS1 paper
4 papers
Approximating Global Contact-Implicit MPC via Sampling and Local Complementarity
Sharanya Venkatesh, Bibit Bianchini, Alp Aydinoglu +2
To achieve general-purpose dexterous manipulation, robots must rapidly devise and execute contact-rich behaviors. Existing model-based controllers are incapable of globally optimiz…
Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
Yuki Shirai, Tong Zhao, H. J. Terry Suh +5
Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesi…
GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients
Brian Plancher, Sabrina M. Neuman, Radhika Ghosal +2
We introduce GRiD: a GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate the nonlinear trajectory optimization subp…
Data-driven Koopman Operators for Model-based Shared Control of Human-Machine Systems
Alexander Broad, Ian Abraham, Todd Murphey +1
We present a data-driven shared control algorithm that can be used to improve a human operator's control of complex dynamic machines and achieve tasks that would otherwise be chall…