13 citations · 28 across the 9 of their papers we have counts for
10 papers
History-Guided Video Diffusion
Kiwhan Song, Boyuan Chen, Max Simchowitz +3
Classifier-free guidance (CFG) is a key technique for improving conditional generation in diffusion models, enabling more accurate control while enhancing sample quality. It is nat…
Faster Algorithms for Growing Collision-Free Convex Polytopes in Robot Configuration Space
Peter Werner, Thomas Cohn, Rebecca H. Jiang +4
We propose two novel algorithms for constructing convex collision-free polytopes in robot configuration space. Finding these polytopes enables the application of stronger motion-pl…
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
Boyuan Chen, Diego Marti Monso, Yilun Du +3
This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels. We apply Diffu…
Robot Fleet Learning via Policy Merging
Lirui Wang, Kaiqing Zhang, Allan Zhou +2
Fleets of robots ingest massive amounts of heterogeneous streaming data silos generated by interacting with their environments, far more than what can be stored or transmitted with…
Constrained Bimanual Planning with Analytic Inverse Kinematics
Thomas Cohn, Seiji Shaw, Max Simchowitz +1
In order for a bimanual robot to manipulate an object that is held by both hands, it must construct motion plans such that the transformation between its end effectors remains fixe…
Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets
Thomas Cohn, Mark Petersen, Max Simchowitz +1
Computing optimal, collision-free trajectories for high-dimensional systems is a challenging problem. Sampling-based planners struggle with the dimensionality, whereas trajectory o…
Globally Convergent Policy Search over Dynamic Filters for Output Estimation
Jack Umenberger, Max Simchowitz, Juan C. Perdomo +2
We introduce the first direct policy search algorithm which provably converges to the globally optimal filter for the classical problem of predicting the outputs…
Do Differentiable Simulators Give Better Policy Gradients?
H. J. Terry Suh, Max Simchowitz, Kaiqing Zhang +1
Differentiable simulators promise faster computation time for reinforcement learning by replacing zeroth-order gradient estimates of a stochastic objective with an estimate based o…
Stabilizing Dynamical Systems via Policy Gradient Methods
Juan C. Perdomo, Jack Umenberger, Max Simchowitz
Stabilizing an unknown control system is one of the most fundamental problems in control systems engineering. In this paper, we provide a simple, model-free algorithm for stabilizi…
Towards a Dimension-Free Understanding of Adaptive Linear Control
Juan C. Perdomo, Max Simchowitz, Alekh Agarwal +1
We study the problem of adaptive control of the linear quadratic regulator for systems in very high, or even infinite dimension. We demonstrate that while sublinear regret requires…