Search Results for author: Nikolay Atanasov

Found 34 papers, 8 papers with code

Latent Policies for Adversarial Imitation Learning

no code implementations22 Jun 2022 Tianyu Wang, Nikhil Karnwal, Nikolay Atanasov

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).

Imitation Learning

DARL1N: Distributed multi-Agent Reinforcement Learning with One-hop Neighbors

1 code implementation18 Feb 2022 Baoqian Wang, Junfei Xie, Nikolay Atanasov

In this paper, we address this limitation by introducing a scalable MARL method called Distributed multi-Agent Reinforcement Learning with One-hop Neighbors (DARL1N).

Multi-agent Reinforcement Learning reinforcement-learning

Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features

no code implementations21 Sep 2021 Thai Duong, Nikolay Atanasov

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

no code implementations ICCV 2021 Mo Shan, Qiaojun Feng, You-Yi Jau, Nikolay Atanasov

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

no code implementations23 Jul 2021 Ehsan Zobeidi, Nikolay Atanasov

Unlike an SDF, which measures distance to the nearest surface in any direction, an SDDF measures distance in a given direction.

Dimensionality Reduction

Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control

no code implementations24 Jun 2021 Thai Duong, Nikolay Atanasov

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.

Total Energy

CORSAIR: Convolutional Object Retrieval and Symmetry-AIded Registration

no code implementations11 Mar 2021 Tianyu Zhao, Qiaojun Feng, Sai Jadhav, Nikolay Atanasov

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

no code implementations10 Mar 2021 Tianyu Wang, Nikolay Atanasov

We employ a spectral learning approach to extract a weighted finite automaton (WFA), approximating the unknown logic structure of the task.

reinforcement-learning

Localization and Mapping using Instance-specific Mesh Models

no code implementations8 Mar 2021 Qiaojun Feng, Yue Meng, Mo Shan, Nikolay Atanasov

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

no code implementations8 Mar 2021 Qiaojun Feng, Nikolay Atanasov

This paper focuses on pose registration of different object instances from the same category.

Metric Learning

Coding for Distributed Multi-Agent Reinforcement Learning

no code implementations7 Jan 2021 Baoqian Wang, Junfei Xie, Nikolay Atanasov

This paper aims to mitigate straggler effects in synchronous distributed learning for multi-agent reinforcement learning (MARL) problems.

Multi-agent Reinforcement Learning reinforcement-learning

Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning

no code implementations1 Jan 2021 Tianyu Wang, Vikas Dhiman, Nikolay Atanasov

The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert's observations and state-control trajectory.

Autonomous Driving Autonomous Navigation +2

Control Barriers in Bayesian Learning of System Dynamics

1 code implementation29 Dec 2020 Vikas Dhiman, Mohammad Javad Khojasteh, Massimo Franceschetti, Nikolay Atanasov

This paper focuses on learning a model of system dynamics online while satisfying safety constraints.

Learning Barrier Functions with Memory for Robust Safe Navigation

no code implementations3 Nov 2020 Kehan Long, Cheng Qian, Jorge Cortés, Nikolay Atanasov

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

1 code implementation15 Sep 2020 Thai Duong, Michael Yip, Nikolay Atanasov

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

2 code implementations29 Jul 2020 Mo Shan, Vikas Dhiman, Qiaojun Feng, Jinzhao Li, Nikolay Atanasov

Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical.

Learning Navigation Costs from Demonstration with Semantic Observations

no code implementations9 Jun 2020 Tianyu Wang, Vikas Dhiman, Nikolay Atanasov

The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert's observations and state-control trajectory.

Autonomous Driving Motion Planning +2

Learning Navigation Costs from Demonstrations with Semantic Observations

no code implementations L4DC 2020 Tianyu Wang, Vikas Dhiman, Nikolay Atanasov

The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert’s observations and state-control trajectory.

Autonomous Driving Motion Planning +1

Safe Robot Navigation in Cluttered Environments using Invariant Ellipsoids and a Reference Governor

no code implementations14 May 2020 Zhichao Li, Thai Duong, Nikolay Atanasov

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

no code implementations26 Feb 2020 Tianyu Wang, Vikas Dhiman, Nikolay Atanasov

This paper focuses on inverse reinforcement learning (IRL) to enable safe and efficient autonomous navigation in unknown partially observable environments.

Autonomous Navigation Motion Planning +1

Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping

1 code implementation5 Feb 2020 Thai Duong, Nikhil Das, Michael Yip, Nikolay Atanasov

This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment.

Autonomous Navigation

Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics

1 code implementation L4DC 2020 Mohammad Javad Khojasteh, Vikas Dhiman, Massimo Franceschetti, Nikolay Atanasov

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

no code implementations23 Oct 2019 Steven W. Chen, Tianyu Wang, Nikolay Atanasov, Vijay Kumar, Manfred Morari

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

2 code implementations7 Oct 2018 Sikang Liu, Kartik Mohta, Nikolay Atanasov, Vijay Kumar

Search-based motion planning has been used for mobile robots in many applications.

Robotics

Dense 3-D Mapping with Spatial Correlation via Gaussian Filtering

no code implementations23 Jan 2018 Ke Sun, Kelsey Saulnier, Nikolay Atanasov, George J. Pappas, Vijay Kumar

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

no code implementations6 Dec 2017 Kartik Mohta, Michael Watterson, Yash Mulgaonkar, Sikang Liu, Chao Qu, Anurag Makineni, Kelsey Saulnier, Ke Sun, Alex Zhu, Jeffrey Delmerico, Konstantinos Karydis, Nikolay Atanasov, Giuseppe Loianno, Davide Scaramuzza, Kostas Daniilidis, Camillo Jose Taylor, Vijay Kumar

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

no code implementations ICLR 2018 Arbaaz Khan, Clark Zhang, Nikolay Atanasov, Konstantinos Karydis, Vijay Kumar, Daniel D. Lee

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

no code implementations7 Jul 2017 Mo Shan, Nikolay Atanasov

The superiority of the proposed spatiotemporal model is demonstrated on the Moving MNIST dataset augmented with rotation and scaling.

Classification General Classification +3

Event-Based Visual Inertial Odometry

no code implementations CVPR 2017 Alex Zihao Zhu, Nikolay Atanasov, Kostas Daniilidis

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

no code implementations23 May 2017 Steven W. Chen, Nikolay Atanasov, Arbaaz Khan, Konstantinos Karydis, Daniel D. Lee, Vijay Kumar

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.

Robot Navigation

Active Deformable Part Models

no code implementations1 Apr 2014 Menglong Zhu, Nikolay Atanasov, George J. Pappas, Kostas Daniilidis

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.

General Classification object-detection +1

Nonmyopic View Planning for Active Object Detection

no code implementations20 Sep 2013 Nikolay Atanasov, Bharath Sankaran, Jerome Le Ny, George J. Pappas, Kostas Daniilidis

One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose.

Active Object Detection object-detection +2

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