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Found 7 papers, 1 papers with code
Hierarchical Policy Blending as Inference for Reactive Robot Control
To combine the benefits of reactive policies and planning, we propose a hierarchical motion generation method.
SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
In this work, we focus on learning SE(3) diffusion models for 6DoF grasping, giving rise to a novel framework for joint grasp and motion optimization without needing to decouple grasp selection from trajectory generation.
Learning Implicit Priors for Motion Optimization
In this paper, we focus on the problem of integrating Energy-based Models (EBM) as guiding priors for motion optimization.
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.
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning
Reactive motion generation problems are usually solved by computing actions as a sum of policies.
Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems
We present a new family of deep neural network-based dynamic systems.
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics.
