26 citations · 91 across the 20 of their papers we have counts for
21 papers
Safe and Steerable Geometric Motion Policies for Robotic Dexterous Manipulation
Albert Wu, Riccardo Bonalli, Thomas Lew +1
Robotic dexterous manipulation requires continuously reconciling objectives and constraints defined on heterogeneous geometric spaces: a robot controlled on a config…
Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle
James Dallas, Thomas Lew, John Talbot +3
Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning-based tools scale to high-dimensional systems, their performance…
Risk-Averse Trajectory Optimization via Sample Average Approximation
Thomas Lew, Riccardo Bonalli, Marco Pavone
Trajectory optimization under uncertainty underpins a wide range of applications in robotics. However, existing methods are limited in terms of reasoning about sources of epistemic…
Convex Hulls of Reachable Sets
Thomas Lew, Riccardo Bonalli, Marco Pavone
We study the convex hulls of reachable sets of nonlinear systems with bounded disturbances and uncertain initial conditions. Reachable sets play a critical role in control, but rem…
Estimating the Convex Hull of the Image of a Set with Smooth Boundary: Error Bounds and Applications
Thomas Lew, Riccardo Bonalli, Lucas Janson +1
We study the problem of estimating the convex hull of the image of a compact set with smooth boundary through a smooth function $f:…
A System-Level View on Out-of-Distribution Data in Robotics
Rohan Sinha, Apoorva Sharma, Somrita Banerjee +6
When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot…
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization
Thomas Lew, Sumeet Singh, Mario Prats +11
We propose a framework to enable multipurpose assistive mobile robots to autonomously wipe tables to clean spills and crumbs. This problem is challenging, as it requires planning w…
Sample Average Approximation for Stochastic Programming with Equality Constraints
Thomas Lew, Riccardo Bonalli, Marco Pavone
We revisit the sample average approximation (SAA) approach for non-convex stochastic programming. We show that applying the SAA approach to problems with expected value equality co…
Robust-RRT: Probabilistically-Complete Motion Planning for Uncertain Nonlinear Systems
Albert Wu, Thomas Lew, Kiril Solovey +2
Robust motion planning entails computing a global motion plan that is safe under all possible uncertainty realizations, be it in the system dynamics, the robot's initial position,…
Data-Driven Chance Constrained Control using Kernel Distribution Embeddings
Adam J. Thorpe, Thomas Lew, Meeko M. K. Oishi +1
We present a data-driven algorithm for efficiently computing stochastic control policies for general joint chance constrained optimal control problems. Our approach leverages the t…
A Simple and Efficient Sampling-based Algorithm for General Reachability Analysis
Thomas Lew, Lucas Janson, Riccardo Bonalli +1
In this work, we analyze an efficient sampling-based algorithm for general-purpose reachability analysis, which remains a notoriously challenging problem with applications ranging…
On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation
Thomas Lew, Apoorva Sharma, James Harrison +2
We identify an issue in recent approaches to learning-based control that reformulate systems with uncertain dynamics using a stochastic differential equation. Specifically, we disc…
Convex Optimization for Trajectory Generation
Danylo Malyuta, Taylor P. Reynolds, Michael Szmuk +4
Reliable and efficient trajectory generation methods are a fundamental need for autonomous dynamical systems of tomorrow. The goal of this article is to provide a comprehensive tut…
Control Barrier Functions for Cyber-Physical Systems and Applications to NMPC
Jan Schilliger, Thomas Lew, Spencer M. Richards +3
Tractable safety-ensuring algorithms for cyber-physical systems are important in critical applications. Approaches based on Control Barrier Functions assume continuous enforcement,…
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
Ali Agha, Kyohei Otsu, Benjamin Morrell +69
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARP…
Sequential Convex Programming For Non-Linear Stochastic Optimal Control
Riccardo Bonalli, Thomas Lew, Marco Pavone
This work introduces a sequential convex programming framework for non-linear, finite-dimensional stochastic optimal control, where uncertainties are modeled by a multidimensional…
Analysis of Theoretical and Numerical Properties of Sequential Convex Programming for Continuous-Time Optimal Control
Riccardo Bonalli, Thomas Lew, Marco Pavone
Sequential Convex Programming (SCP) has recently gained significant popularity as an effective method for solving optimal control problems and has been successfully applied in seve…
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework
Thomas Lew, Apoorva Sharma, James Harrison +2
Safe deployment of autonomous robots in diverse scenarios requires agents that are capable of efficiently adapting to new environments while satisfying constraints. In this work, w…
Sampling-based Reachability Analysis: A Random Set Theory Approach with Adversarial Sampling
Thomas Lew, Marco Pavone
Reachability analysis is at the core of many applications, from neural network verification, to safe trajectory planning of uncertain systems. However, this problem is notoriously…
Contact Inertial Odometry: Collisions are your Friends
Thomas Lew, Tomoki Emmei, David D. Fan +4
Autonomous exploration of unknown environments with aerial vehicles remains a challenge, especially in perceptually degraded conditions. Dust, fog, or a lack of visual or LiDAR-bas…
Trajectory Optimization on Manifolds: A Theoretically-Guaranteed Embedded Sequential Convex Programming Approach
Riccardo Bonalli, Andrew Bylard, Abhishek Cauligi +2
Sequential Convex Programming (SCP) has recently gained popularity as a tool for trajectory optimization due to its sound theoretical properties and practical performance. Yet, mos…