98 citations · 307 across the 12 of their papers we have counts for
12 papers
Reactive Base Control for On-The-Move Mobile Manipulation in Dynamic Environments
Ben Burgess-Limerick, Jesse Haviland, Chris Lehnert +1
We present a reactive base control method that enables high performance mobile manipulation on-the-move in environments with static and dynamic obstacles. Performing manipulation t…
SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
Krishan Rana, Jesse Haviland, Sourav Garg +3
Large language models (LLMs) have demonstrated impressive results in developing generalist planning agents for diverse tasks. However, grounding these plans in expansive, multi-flo…
Manipulator Differential Kinematics: Part 1: Kinematics, Velocity, and Applications
Jesse Haviland, Peter Corke
Manipulator kinematics is concerned with the motion of each link within a manipulator without considering mass or force. In this article, which is the first in a two-part tutorial,…
Manipulator Differential Kinematics: Part 2: Acceleration and Advanced Applications
Jesse Haviland, Peter Corke
This is the second and final article on the tutorial on manipulator differential kinematics. In Part 1, we described a method of modelling kinematics using the elementary transform…
Visibility Maximization Controller for Robotic Manipulation
Kerry He, Rhys Newbury, Tin Tran +5
Occlusions caused by a robot's own body is a common problem for closed-loop control methods employed in eye-to-hand camera setups. We propose an optimization-based reactive control…
Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots
Krishan Rana, Vibhavari Dasagi, Jesse Haviland +3
While deep reinforcement learning (RL) agents have demonstrated incredible potential in attaining dexterous behaviours for robotics, they tend to make errors when deployed in the r…
A Holistic Approach to Reactive Mobile Manipulation
Jesse Haviland, Niko Sünderhauf, Peter Corke
We present the design and implementation of a taskable reactive mobile manipulation system. In contrary to related work, we treat the arm and base degrees of freedom as a holistic…
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics
Krishan Rana, Vibhavari Dasagi, Jesse Haviland +3
We present Bayesian Controller Fusion (BCF): a hybrid control strategy that combines the strengths of traditional hand-crafted controllers and model-free deep reinforcement learnin…
A Systematic Approach to Computing the Manipulator Jacobian and Hessian using the Elementary Transform Sequence
Jesse Haviland, Peter Corke
The elementary transform sequence (ETS) provides a universal method of describing the kinematics of any serial-link manipulator. The ETS notation is intuitive and easy to understan…
NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators
Jesse Haviland, Peter Corke
We present NEO, a fast and purely reactive motion controller for manipulators which can avoid static and dynamic obstacles while moving to the desired end-effector pose. Additional…
Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing
Jesse Haviland, Feras Dayoub, Peter Corke
This paper considers the final approach phase of visual-closed-loop grasping where the RGB-D camera is no longer able to provide valid depth information. Many current robotic grasp…
A Purely-Reactive Manipulability-Maximising Motion Controller
Jesse Haviland, Peter Corke
We present a novel approach to controlling the instantaneous velocity of a robot end-effector that is able to simultaneously maximise manipulability and avoid joint limits. It oper…