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Found 5 papers, 0 papers with code
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 having models that are able to represent 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.
