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Found 33 papers, 7 papers with code
Disturbance Observers for Robust Backup Control Barrier Functions
To tackle this shortcoming, we integrate backup control barrier functions with a disturbance observer and estimate the flow under a reconstruction of the disturbance while refining this estimate over time.
Generative Predictive Control: Flow Matching Policies for Dynamic and Difficult-to-Demonstrate Tasks
In particular, we introduce generative predictive control, a supervised learning framework for tasks with fast dynamics that are easy to simulate but difficult to demonstrate.
Minimizing Conservatism in Safety-Critical Control for Input-Delayed Systems via Adaptive Delay Estimation
The main idea is to reduce the maximum delay estimation error bound so that the state prediction error bound is monotonically non-increasing.
Zero-Order Control Barrier Functions for Sampled-Data Systems with State and Input Dependent Safety Constraints
We propose a novel zero-order control barrier function (ZOCBF) for sampled-data systems to ensure system safety.
Supervised Learning for Stochastic Optimal Control
In this paper, we show how to automatically generate supervised learning data for a class of continuous-time nonlinear stochastic optimal control problems.
Rollover Prevention for Mobile Robots with Control Barrier Functions: Differentiator-Based Adaptation and Projection-to-State Safety
This paper develops rollover prevention guarantees for mobile robots using control barrier function (CBF) theory, and demonstrates the method experimentally.
Robust Control Barrier Functions using Uncertainty Estimation with Application to Mobile Robots
Our constructive framework couples control barrier function (CBF) theory with a new uncertainty estimator to ensure robust safety.
Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data
Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face.
Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification
The dramatic increase of autonomous systems subject to variable environments has given rise to the pressing need to consider risk in both the synthesis and verification of policies for these systems.
Risk-Averse Receding Horizon Motion Planning for Obstacle Avoidance using Coherent Risk Measures
This paper studies the problem of risk-averse receding horizon motion planning for agents with uncertain dynamics, in the presence of stochastic, dynamic obstacles.
Distributionally Robust Model Predictive Control with Total Variation Distance
This paper studies the problem of distributionally robust model predictive control (MPC) using total variation distance ambiguity sets.
Risk-Averse Stochastic Shortest Path Planning
We consider the stochastic shortest path planning problem in MDPs, i. e., the problem of designing policies that ensure reaching a goal state from a given initial state with minimum accrued cost.
Learning Invariant Representation of Tasks for Robust Surgical State Estimation
The combination of high diversity and limited data calls for new learning methods that are robust and invariant to operating conditions and surgical techniques.
Risk-Sensitive Motion Planning using Entropic Value-at-Risk
We consider the problem of risk-sensitive motion planning in the presence of randomly moving obstacles.
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
ROIAL learns Bayesian posteriors that predict each exoskeleton user's utility landscape across four exoskeleton gait parameters.
daVinciNet: Joint Prediction of Motion and Surgical State in Robot-Assisted Surgery
We propose daVinciNet - an end-to-end dual-task model for robot motion and surgical state predictions.
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties
Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles.
Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space.
Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources
A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs.
Stochastic Finite State Control of POMDPs with LTL Specifications
Partially observable Markov decision processes (POMDPs) provide a modeling framework for autonomous decision making under uncertainty and imperfect sensing, e. g. robot manipulation and self-driving cars.
Dueling Posterior Sampling for Preference-Based Reinforcement Learning
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback.
Control Regularization for Reduced Variance Reinforcement Learning
We show that functional regularization yields a bias-variance trade-off, and propose an adaptive tuning strategy to optimize this trade-off.
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks
Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process.
Stagewise Safe Bayesian Optimization with Gaussian Processes
We provide theoretical guarantees for both the satisfaction of safety constraints as well as convergence to the optimal utility value.
Quantifying Performance of Bipedal Standing with Multi-channel EMG
Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function.
Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis
Observing that each demonstrator has an inherent reward for each state and the task-specific behaviors mainly depend on a small number of key states, we propose a meta IRL algorithm that first models the reward function for each task as a distribution conditioned on a baseline reward function shared by all tasks and dependent only on the demonstrator, and then finds the most likely reward function in the distribution that explains the task-specific behaviors.
A Function Approximation Method for Model-based High-Dimensional Inverse Reinforcement Learning
This works handles the inverse reinforcement learning problem in high-dimensional state spaces, which relies on an efficient solution of model-based high-dimensional reinforcement learning problems.
Inverse Reinforcement Learning in Large State Spaces via Function Approximation
We also show that the proposed method can extend many existing methods to high-dimensional state spaces.
Bellman Gradient Iteration for Inverse Reinforcement Learning
We introduce a strategy to flexibly handle different types of actions with two approximations of the Bellman Optimality Equation, and a Bellman Gradient Iteration method to compute the gradient of the Q-value with respect to the reward function.
Clinical Patient Tracking in the Presence of Transient and Permanent Occlusions via Geodesic Feature
This paper develops a method to use RGB-D cameras to track the motions of a human spinal cord injury patient undergoing spinal stimulation and physical rehabilitation.
Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces
This problem can be formulated as a $K$-armed Dueling Bandits problem where $K$ is the total number of decisions.
Multi-dueling Bandits with Dependent Arms
The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly well-suited for modeling settings that elicit subjective or implicit human feedback.
Convex Relaxations of SE(2) and SE(3) for Visual Pose Estimation
The method is a convex relaxation of the classical pose estimation problem, and is based on explicit linear matrix inequality (LMI) representations for the convex hulls of $SE(2)$ and $SE(3)$.
