activity
20092022
most citedGuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming

119 citations · 355 across the 60 of their papers we have counts for

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Showing 2022Show all

23 papers · 1 filter

eess.SY2022

Adaptive Robust Model Predictive Control via Uncertainty Cancellation

Rohan Sinha, James Harrison, Spencer M. Richards +1

We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally…

cs.LG20221 cited

Foundation Models for Semantic Novelty in Reinforcement Learning

Tarun Gupta, Peter Karkus, Tong Che +2

Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address this challenge by defining a novel intrinsic reward based on a foundation model,…

cs.RO20224 cited

Guided Conditional Diffusion for Controllable Traffic Simulation

Ziyuan Zhong, Davis Rempe, Danfei Xu +5

Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles. Typical heuristic-based traffic models offer flexible control to make ve…

cs.GT2022

Credit-Based Congestion Pricing: Equilibrium Properties and Optimal Scheme Design

Devansh Jalota, Jessica Lazarus, Alexandre Bayen +1

Credit-based congestion pricing (CBCP) has emerged as a mechanism to alleviate the social inequity concerns of road congestion pricing - a promising strategy for traffic congestion…

cs.LG2022

Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models

Filippos Christianos, Peter Karkus, Boris Ivanovic +2

Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles. Recent deep learning models have shown impressive results for predictin…

eess.SY2022

Differentially Private Stochastic Convex Optimization for Network Routing Applications

Matthew Tsao, Karthik Gopalakrishnan, Kaidi Yang +1

Network routing problems are common across many engineering applications. Computing optimal routing policies requires knowledge about network demand, i.e., the origin and destinati…