173 citations · 876 across the 56 of their papers we have counts for
10 papers · 1 filter
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks
Daniel Seita, Pete Florence, Jonathan Tompson +4
Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a long-standing challenge in robotic manipulation. The complex dynamics and high-dimensional co…
Safely Learning Dynamical Systems from Short Trajectories
Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani +1
A fundamental challenge in learning to control an unknown dynamical system is to reduce model uncertainty by making measurements while maintaining safety. In this work, we formulat…
Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles
Bachir El Khadir, Jean Bernard Lasserre, Vikas Sindhwani
We propose a novel method for planning shortest length piecewise-linear motions through complex environments punctured with static, moving, or even morphing obstacles. Using a mome…
Transporter Networks: Rearranging the Visual World for Robotic Manipulation
Andy Zeng, Pete Florence, Jonathan Tompson +9
Robotic manipulation can be formulated as inducing a sequence of spatial displacements: where the space being moved can encompass an object, part of an object, or end effector. In…
Learning Stability Certificates from Data
Nicholas M. Boffi, Stephen Tu, Nikolai Matni +2
Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function that guara…
An Ode to an ODE
Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov +6
We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d…