no code implementations • 11 Jul 2019 • Weiming Zhi, Lionel Ott, Fabio Ramos
Understanding the dynamics of an environment, such as the movement of humans and vehicles, is crucial for agents to achieve long-term autonomy in urban environments.
no code implementations • 5 Sep 2019 • Tin Lai, Weiming Zhi, Fabio Ramos
Trajectory modelling had been the principal research area for understanding and anticipating human behaviour.
no code implementations • 25 Sep 2019 • Weiming Zhi, Tin Lai, Lionel Ott, Gilad Francis, Fabio Ramos
This generally involves the prediction and understanding of motion patterns of dynamic entities, such as vehicles and people, in the surroundings.
no code implementations • 12 Nov 2020 • Weiming Zhi, Tin Lai, Lionel Ott, Fabio Ramos
Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements.
no code implementations • 4 Jul 2021 • Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos
Advances in differentiable numerical integrators have enabled the use of gradient descent techniques to learn ordinary differential equations (ODEs).
no code implementations • 9 Jul 2021 • Weiming Zhi, Lionel Ott, Fabio Ramos
This distribution is then used as a prior to a constrained optimisation problem which enforces chance constraints on the trajectory distribution.
no code implementations • 26 Aug 2021 • Tin Lai, Weiming Zhi, Tucker Hermans, Fabio Ramos
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP).
no code implementations • 29 Sep 2021 • Weiming Zhi, Tin Lai, Lionel Ott, Edwin V Bonilla, Fabio Ramos
Consequently, by restricting the base ODE to be amenable to integration, we can speed up and improve the robustness of integrating trajectories from the learned system.
no code implementations • 2 Mar 2022 • Tin Lai, Weiming Zhi, Tucker Hermans, Fabio Ramos
We study the kinodynamic variants of tree-based planning, where we have known system dynamics and kinematic constraints.
no code implementations • 7 Sep 2023 • Weiming Zhi, Tianyi Zhang, Matthew Johnson-Roberson
Diagrammatic Teaching aims to teach robots novel skills by prompting the user to sketch out demonstration trajectories on 2D images of the scene, these are then synthesised as a generative model of motion trajectories in 3D task space.
no code implementations • 19 Sep 2023 • Weiming Zhi, Kangni Liu, Tianyi Zhang, Matthew Johnson-Roberson
In this work, we tackle the problem of teaching a robot to approach a surface and then follow cyclic motion on it, where the cycle of the motion can be arbitrarily specified by a single user-provided sketch over an image from the robot's camera.
1 code implementation • 16 Mar 2024 • Tianyi Zhang, Kaining Huang, Weiming Zhi, Matthew Johnson-Roberson
Humans have the remarkable ability to construct consistent mental models of an environment, even under limited or varying levels of illumination.