Se (3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
Multi-objective optimization problems are ubiquitous in robotics, eg, the optimization of a
robot manipulation task requires a joint consideration of grasp pose configurations …
robot manipulation task requires a joint consideration of grasp pose configurations …
Motion planning diffusion: Learning and planning of robot motions with diffusion models
Learning priors on trajectory distributions can help accelerate robot motion planning
optimization. Given previously successful plans, learning trajectory generative models as …
optimization. Given previously successful plans, learning trajectory generative models as …
Constrained stein variational trajectory optimization
T Power, D Berenson - IEEE Transactions on Robotics, 2024 - ieeexplore.ieee.org
In this article, we present constrained Stein variational trajectory optimization (CSVTO), an
algorithm for performing trajectory optimization with constraints on a set of trajectories in …
algorithm for performing trajectory optimization with constraints on a set of trajectories in …
Accelerating motion planning via optimal transport
AT Le, G Chalvatzaki, A Biess… - Advances in Neural …, 2024 - proceedings.neurips.cc
Motion planning is still an open problem for many disciplines, eg, robotics, autonomous
driving, due to their need for high computational resources that hinder real-time, efficient …
driving, due to their need for high computational resources that hinder real-time, efficient …
Hierarchical policy blending as inference for reactive robot control
Motion generation in cluttered, dense, and dynamic environments is a central topic in
robotics, rendered as a multi-objective decision-making problem. Current approaches trade …
robotics, rendered as a multi-objective decision-making problem. Current approaches trade …
Motion planning diffusion: Learning and adapting robot motion planning with diffusion models
The performance of optimization-based robot motion planning algorithms is highly
dependent on the initial solutions, commonly obtained by running a sampling-based planner …
dependent on the initial solutions, commonly obtained by running a sampling-based planner …
Revisiting energy based models as policies: Ranking noise contrastive estimation and interpolating energy models
A crucial design decision for any robot learning pipeline is the choice of policy
representation: what type of model should be used to generate the next set of robot actions …
representation: what type of model should be used to generate the next set of robot actions …
Sampling constrained trajectories using composable diffusion models
Trajectory optimization and optimal control are powerful tools for synthesizing complex robot
behavior using appropriate cost functions and constraints. However, methods for solving the …
behavior using appropriate cost functions and constraints. However, methods for solving the …
Preconditioners for the Stochastic Training of Implicit Neural Representations
SF Chng, H Saratchandran, S Lucey - arxiv preprint arxiv:2402.08784, 2024 - arxiv.org
Implicit neural representations have emerged as a powerful technique for encoding complex
continuous multidimensional signals as neural networks, enabling a wide range of …
continuous multidimensional signals as neural networks, enabling a wide range of …
Learning stable vector fields on lie groups
Learning robot motions from demonstration requires models able to specify vector fields for
the full robot pose when the task is defined in operational space. Recent advances in …
the full robot pose when the task is defined in operational space. Recent advances in …