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Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions
This paper proves asymptotic optimality for a number of variations on FMT*, namely when the configuration space is sampled non-uniformly, when the cost is not arc length, and when connections are made based on the number of nearest neighbors instead of a fixed connection radius. Expand
The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs
  • B. Ivanovic, M. Pavone
  • Computer Science
  • IEEE/CVF International Conference on Computer…
  • 14 October 2018
The Trajectron is presented, a graph-structured model that predicts many potential future trajectories of multiple agents simultaneously in both highly dynamic and multimodal scenarios (i.e. where the number of agents in the scene is time-varying and there are many possible highly-distinct futures for each agent). Expand
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
This paper derives a formula for computing the gradient of the Lagrangian function for percentile risk-constrained Markov decision processes and devise policy gradient and actor-critic algorithms that estimate such gradient, update the policy in the descent direction, and update the Lagrange multiplier in the ascent direction. Expand
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
This paper shows that a CVaR objective, besides capturing risk sensitivity, has an alternative interpretation as expected cost under worst-case modeling errors, for a given error budget, and presents an approximate value-iteration algorithm forCVaR MDPs and analyzes its convergence rate. Expand
Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data
Trajectron++ is a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents while incorporating agent dynamics and heterogeneous data and outperforming a wide array of state-of-the-art deterministic and generative methods. Expand
Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control
Trajectron++ is a modular, graph-structured recurrent model that forecasts the trajectories of a general number of agents with distinct semantic classes while incorporating heterogeneous data (e.g. semantic maps and camera images) and is designed to be tightly integrated with robotic planning and control frameworks. Expand
Learning Sampling Distributions for Robot Motion Planning
This paper proposes a methodology for nonuniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling, resulting in an order of magnitude improvement in terms of success rate and convergence to the optimal cost. Expand
Decentralized Policies for Geometric Pattern Formation and Path Coverage
This paper presents a decentralized control policy for symmetric formations in multiagent systems. It is shown that n agents, each one pursuing its leading neighbor along the line of sight rotated byExpand
Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore
Using actual transportation data, this analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately 1/3 of the total number of passenger vehicles currently in operation. Expand
Control of robotic mobility-on-demand systems: A queueing-theoretical perspective
This paper provides a rigorous approach to the problem of system-wide coordination of autonomously driving vehicles, and provides one of the first characterizations of the sustainability benefits of robotic transportation networks. Expand