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Found 10 papers, 3 papers with code
Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods
This paper presents a novel learning approach for Dubins Traveling Salesman Problems(DTSP) with Neighborhood (DTSPN) to quickly produce a tour of a non-holonomic vehicle passing through neighborhoods of given task points.
Computing Forward Reachable Sets for Nonlinear Adaptive Multirotor Controllers
By compensating for disturbances with the adaptive controller, our over-approximated FRS can be smaller than other ellipsoidal over-approximations.
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly.
Learning to Reason: Distilling Hierarchy via Self-Supervision and Reinforcement Learning
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly.
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional sequential raw data, e. g., video.
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics
To resolve the challenge, this paper proposes a framework using multiple GP transition models which is capable of describing multi-modal dynamics.
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional \textit{sequential} raw data, e. g., video.
Deep Gaussian Process-Based Bayesian Inference for Contaminant Source Localization
This paper proposes a Bayesian framework for localization of multiple sources in the event of accidental hazardous contaminant release.
Applications
Approximate Inference-based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models
Second, an approximate inference algorithm is used, exploiting through the duality between control and estimation, to explore the decision space and to compute a high-quality motion trajectory of the robot.
Multiscale Inverse Reinforcement Learning using Diffusion Wavelets
This work presents a multiscale framework to solve an inverse reinforcement learning (IRL) problem for continuous-time/state stochastic systems.
