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Found 9 papers, 0 papers with code
L4KDE: Learning for KinoDynamic Tree Expansion
We study the kinodynamic variants of tree-based planning, where we have known system dynamics and kinematic constraints.
Learning Efficient and Robust Ordinary Differential Equations via Diffeomorphisms
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.
Parallelised Diffeomorphic Sampling-based Motion Planning
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP).
Probabilistic Trajectory Prediction with Structural Constraints
This distribution is then used as a prior to a constrained optimisation problem which enforces chance constraints on the trajectory distribution.
Learning ODEs via Diffeomorphisms for Fast and Robust Integration
Advances in differentiable numerical integrators have enabled the use of gradient descent techniques to learn ordinary differential equations (ODEs).
Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
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.
OCTNet: Trajectory Generation in New Environments from Past Experiences
This generally involves the prediction and understanding of motion patterns of dynamic entities, such as vehicles and people, in the surroundings.
Occ-Traj120: Occupancy Maps with Associated Trajectories
Trajectory modelling had been the principal research area for understanding and anticipating human behaviour.
Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction
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.
