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Arthur Richards

8 accepted papers

2023

Mode-constrained Model-based Reinforcement Learning via Gaussian Processes

AISTATS 2023poster

Model-based reinforcement learning (RL) algorithms do not typically consider environments with multiple dynamic modes, where it is beneficial to avoid inoperable or undesirable modes. We present a model-based RL algorithm that constrains training to a single dynamic mode with high probability. This…

2021

Fast Generation of Obstacle-Avoiding Motion Primitives for Quadrotors

IROS 2021poster

This work considers the problem of generating computationally efficient quadrotor motion primitives between a given pose (position, velocity, and acceleration) and a goal plane in the presence of obstacles. A new motion primitive tool based on the logistic curve is proposed and a closed-form analyti…

Cited by 1SourceScholar
2021

Reactive Visual Odometry Scheduling Based on Noise Analysis using an Adaptive Extended Kalman Filter

IROS 2021poster

A new strategy is proposed for scheduling Visual Odometry (VO) measurements for wheeled ground vehicles. Rather than having a fixed interval or distance between image acquisitions, we propose to trigger VO based on covariances from an Adaptive Extended Kalman Filter. The adopted model uses process n…

Cited by 1SourceScholar
2021

Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation

ICRA 2021poster

This paper presents a two-stage method to perform trajectory optimisation in multimodal dynamical systems with unknown nonlinear stochastic transition dynamics. The method finds trajectories that remain in a preferred dynamics mode where possible and in regions of the transition dynamics model that…

Cited by 12SourceScholar
2017

Robot navigation using convex model predictive control and approximate operating region optimization

IROS 2017poster

A method for real-time robot navigation with obstacle avoidance is presented. A two-stage approach is proposed: the use of online Simulated Annealing (SA) to optimize a convex operating region in configuration space is paired with Model Predictive Control (MPC) to determine a locally optimal motion…

Cited by 4SourceScholar