no code implementations • 24 Jun 2023 • H. J. Terry Suh, Glen Chou, Hongkai Dai, Lujie Yang, Abhishek Gupta, Russ Tedrake
However, in order to apply them effectively in offline optimization paradigms such as offline Reinforcement Learning (RL) or Imitation Learning (IL), we require a more careful consideration of how uncertainty estimation interplays with first-order methods that attempt to minimize them.
no code implementations • 24 Apr 2023 • Glen Chou, Russ Tedrake
To solve this problem approximately, we propose two approaches: the first solves a sequence of sum-of-squares optimization problems to iteratively improve a policy which is provably-stable by construction, while the second directly performs gradient-based optimization on the parameters of the polynomial policy, and its closed-loop stability is verified a posteriori.
1 code implementation • 9 Mar 2023 • Jiayi Pan, Glen Chou, Dmitry Berenson
We evaluate our approach on three existing LTL/natural language datasets and show that we can translate natural language commands at 75\% accuracy with far less human data ($\le$12 annotations).
no code implementations • 13 Dec 2022 • Craig Knuth, Glen Chou, Jamie Reese, Joe Moore
We present a method for providing statistical guarantees on runtime safety and goal reachability for integrated planning and control of a class of systems with unknown nonlinear stochastic underactuated dynamics.
no code implementations • 14 Jun 2022 • Glen Chou, Necmiye Ozay, Dmitry Berenson
We present a motion planning algorithm for a class of uncertain control-affine nonlinear systems which guarantees runtime safety and goal reachability when using high-dimensional sensor measurements (e. g., RGB-D images) and a learned perception module in the feedback control loop.
no code implementations • 8 Dec 2021 • Glen Chou, Hao Wang, Dmitry Berenson
We propose a method for learning constraints represented as Gaussian processes (GPs) from locally-optimal demonstrations.
no code implementations • 18 Apr 2021 • Glen Chou, Necmiye Ozay, Dmitry Berenson
We derive a trajectory tracking error bound for a contraction-based controller that is subjected to this model error, and then learn a controller that optimizes this tracking bound.
no code implementations • 9 Nov 2020 • Glen Chou, Necmiye Ozay, Dmitry Berenson
We present a method for learning to satisfy uncertain constraints from demonstrations.
no code implementations • 18 Oct 2020 • Craig Knuth, Glen Chou, Necmiye Ozay, Dmitry Berenson
Our method imposes the feedback law existence as a constraint in a sampling-based planner, which returns a feedback policy around a nominal plan ensuring that, if the Lipschitz constant estimate is valid, the true system is safe during plan execution, reaches the goal, and is ultimately invariant in a small set about the goal.
no code implementations • 3 Jun 2020 • Glen Chou, Necmiye Ozay, Dmitry Berenson
We present a method for learning multi-stage tasks from demonstrations by learning the logical structure and atomic propositions of a consistent linear temporal logic (LTL) formula.
no code implementations • 25 Jan 2020 • Glen Chou, Necmiye Ozay, Dmitry Berenson
We present an algorithm for learning parametric constraints from locally-optimal demonstrations, where the cost function being optimized is uncertain to the learner.
no code implementations • 8 Oct 2019 • Glen Chou, Necmiye Ozay, Dmitry Berenson
We present a scalable algorithm for learning parametric constraints in high dimensions from safe expert demonstrations.
no code implementations • 17 Dec 2018 • Glen Chou, Dmitry Berenson, Necmiye Ozay
We also provide theoretical analysis on what subset of the constraint can be learnable from safe demonstrations.
no code implementations • 10 Nov 2016 • Frank Jiang, Glen Chou, Mo Chen, Claire J. Tomlin
To sidestep the curse of dimensionality when computing solutions to Hamilton-Jacobi-Bellman partial differential equations (HJB PDE), we propose an algorithm that leverages a neural network to approximate the value function.