Search Results for author:
Found 34 papers, 8 papers with code
Latent Policies for Adversarial Imitation Learning
We use an action encoder-decoder model to obtain a low-dimensional latent action space and train a LAtent Policy using Adversarial imitation Learning (LAPAL).
TerrainMesh: Metric-Semantic Terrain Reconstruction from Aerial Images Using Joint 2D-3D Learning
A local mesh is reconstructed using an initialization and refinement stage.
DARL1N: Distributed multi-Agent Reinforcement Learning with One-hop Neighbors
In this paper, we address this limitation by introducing a scalable MARL method called Distributed multi-Agent Reinforcement Learning with One-hop Neighbors (DARL1N).
Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features
Adaptive control is a critical component of reliable robot autonomy in rapidly changing operational conditions.
ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description
It is compact because it relies on a low-dimensional latent representation of implicit object shape, allowing onboard storage of large multi-category object maps.
A Deep Signed Directional Distance Function for Object Shape Representation
Unlike an SDF, which measures distance to the nearest surface in any direction, an SDDF measures distance in a given direction.
Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control
This paper proposes a Hamiltonian formulation over the SE(3) manifold of the structure of a neural ordinary differential equation (ODE) network to approximate the dynamics of a rigid body.
CORSAIR: Convolutional Object Retrieval and Symmetry-AIded Registration
This paper considers online object-level mapping using partial point-cloud observations obtained online in an unknown environment.
WFA-IRL: Inverse Reinforcement Learning of Autonomous Behaviors Encoded as Weighted Finite Automata
We employ a spectral learning approach to extract a weighted finite automaton (WFA), approximating the unknown logic structure of the task.
Localization and Mapping using Instance-specific Mesh Models
We show that the errors between projections of the mesh model and the observed keypoints and masks can be differentiated in order to obtain accurate instance-specific object shapes.
Fully Convolutional Geometric Features for Category-level Object Alignment
This paper focuses on pose registration of different object instances from the same category.
Coding for Distributed Multi-Agent Reinforcement Learning
This paper aims to mitigate straggler effects in synchronous distributed learning for multi-agent reinforcement learning (MARL) problems.
Mesh Reconstruction from Aerial Images for Outdoor Terrain Mapping Using Joint 2D-3D Learning
Each local mesh is initialized from sparse depth measurements.
Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning
The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert's observations and state-control trajectory.
Control Barriers in Bayesian Learning of System Dynamics
This paper focuses on learning a model of system dynamics online while satisfying safety constraints.
Learning Barrier Functions with Memory for Robust Safe Navigation
Control barrier functions are widely used to enforce safety properties in robot motion planning and control.
Motion Planning
Robotics
Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping
This paper focuses on online occupancy mapping and real-time collision checking onboard an autonomous robot navigating in a large unknown environment.
Robotics
OrcVIO: Object residual constrained Visual-Inertial Odometry
Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical.
Learning Navigation Costs from Demonstration with Semantic Observations
The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert's observations and state-control trajectory.
Learning Navigation Costs from Demonstrations with Semantic Observations
The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert’s observations and state-control trajectory.
Safe Robot Navigation in Cluttered Environments using Invariant Ellipsoids and a Reference Governor
This paper considers the problem of safe autonomous navigation in unknown environments, relying on local obstacle sensing.
Systems and Control Robotics Systems and Control
Learning Navigation Costs from Demonstration in Partially Observable Environments
This paper focuses on inverse reinforcement learning (IRL) to enable safe and efficient autonomous navigation in unknown partially observable environments.
Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment.
Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics
This paper focuses on learning a model of system dynamics online while satisfying safety constraints. Our motivation is to avoid offline system identification or hand-specified dynamics models and allowa system to safely and autonomously estimate and adapt its own model during online operation. Given streaming observations of the system state, we use Bayesian learning to obtain a distributionover the system dynamics.
Large Scale Model Predictive Control with Neural Networks and Primal Active Sets
The approach combines an offline-trained fully-connected neural network with an online primal active set solver.
Towards Search-based Motion Planning for Micro Aerial Vehicles
Search-based motion planning has been used for mobile robots in many applications.
Robotics
Dense 3-D Mapping with Spatial Correlation via Gaussian Filtering
Many accurate and efficient methods exist that address this problem but most assume that the occupancy states of different elements in the map representation are statistically independent.
Robotics
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment.
Robotics
Memory Augmented Control Networks
The third part uses a network controller that learns to store those specific instances of past information that are necessary for planning.
A spatiotemporal model with visual attention for video classification
The superiority of the proposed spatiotemporal model is demonstrated on the Moving MNIST dataset augmented with rotation and scaling.
Event-Based Visual Inertial Odometry
An Extended Kalman Filter with a structureless measurement model then fuses the feature tracks with the output of the IMU.
Neural Network Memory Architectures for Autonomous Robot Navigation
This work is a first thorough study of memory structures for deep-neural-network-based robot navigation, and offers novel tools to train such networks from supervision and quantify their ability to generalize to unseen scenarios.
Active Deformable Part Models
This paper presents an active approach for part-based object detection, which optimizes the order of part filter evaluations and the time at which to stop and make a prediction.
Nonmyopic View Planning for Active Object Detection
One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose.
