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Found 14 papers, 1 papers with code
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching
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.
Synthesizing Stable Reduced-Order Visuomotor Policies for Nonlinear Systems via Sums-of-Squares Optimization
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.
Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification
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).
Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems
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.
Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory
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.
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations
We propose a method for learning constraints represented as Gaussian processes (GPs) from locally-optimal demonstrations.
Model Error Propagation via Learned Contraction Metrics for Safe Feedback Motion Planning of Unknown Systems
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.
Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations
We present a method for learning to satisfy uncertain constraints from demonstrations.
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants
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.
Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal Demonstrations
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.
Learning Constraints from Locally-Optimal Demonstrations under Cost Function Uncertainty
We present an algorithm for learning parametric constraints from locally-optimal demonstrations, where the cost function being optimized is uncertain to the learner.
Learning Parametric Constraints in High Dimensions from Demonstrations
We present a scalable algorithm for learning parametric constraints in high dimensions from safe expert demonstrations.
Learning Constraints from Demonstrations
We also provide theoretical analysis on what subset of the constraint can be learnable from safe demonstrations.
Using Neural Networks to Compute Approximate and Guaranteed Feasible Hamilton-Jacobi-Bellman PDE Solutions
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.
