Search Results for author:
Found 18 papers, 5 papers with code
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features
We propose DistillNeRF, a self-supervised learning framework addressing the challenge of understanding 3D environments from limited 2D observations in autonomous driving.
Interactive Joint Planning for Autonomous Vehicles
In particular, IJP jointly optimizes over the behavior of the ego and the surrounding agents and leverages deep-learned prediction models as prediction priors that the join trajectory optimization tries to stay close to.
Partial-View Object View Synthesis via Filtered Inversion
At inference, given one or more views of a novel real-world object, FINV first finds a set of latent codes for the object by inverting the generative model from multiple initial seeds.
DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles
To enable the joint optimization of AV stacks while retaining modularity, we present DiffStack, a differentiable and modular stack for prediction, planning, and control.
Foundation Models for Semantic Novelty in Reinforcement Learning
Effectively exploring the environment is a key challenge in reinforcement learning (RL).
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles.
Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation
We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar robot navigation in previously unseen indoor environments.
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban
We explore whether integrated tasks like Mujoban can be solved by composing RL modules together in a sense-plan-act hierarchy, where modules have well-defined roles similarly to classic robot architectures.
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
To encourage progress towards this goal we introduce a set of physically embedded planning problems and make them publicly available.
Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization
We address these tasks by combining spatial structure (differentiable mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN).
Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations
The particle filter maintains a belief using learned discriminative update, which is trained end-to-end for decision making.
Particle Filter Recurrent Neural Networks
Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data.
Differentiable Algorithm Networks for Composable Robot Learning
This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems.
Factored Contextual Policy Search with Bayesian Optimization
Our key observation is that experience can be directly generalized over target contexts.
Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation
We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i. e. a deep neural network.
Particle Filter Networks with Application to Visual Localization
Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc.
QMDP-Net: Deep Learning for Planning under Partial Observability
It is a recurrent policy network, but it represents a policy for a parameterized set of tasks by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure of planning in a network learning architecture.
Factored Contextual Policy Search with Bayesian Optimization
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different "contexts".
