Search Results for author: Nikolay Atanasov

Found 45 papers, 16 papers with code

Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space Alignment

1 code implementation4 Jun 2024 Tianyu Wang, Dwait Bhatt, Xiaolong Wang, Nikolay Atanasov

We first introduce encoders and decoders to associate the states and actions of the source robot with a latent space.

Decoder Reinforcement Learning (RL) +1

Distributionally Robust Policy and Lyapunov-Certificate Learning

1 code implementation3 Apr 2024 Kehan Long, Jorge Cortes, Nikolay Atanasov

This article presents novel methods for synthesizing distributionally robust stabilizing neural controllers and certificates for control systems under model uncertainty.

Learning Generalizable Feature Fields for Mobile Manipulation

no code implementations12 Mar 2024 Ri-Zhao Qiu, Yafei Hu, Ge Yang, Yuchen Song, Yang Fu, Jianglong Ye, Jiteng Mu, Ruihan Yang, Nikolay Atanasov, Sebastian Scherer, Xiaolong Wang

An open problem in mobile manipulation is how to represent objects and scenes in a unified manner, so that robots can use it both for navigating in the environment and manipulating objects.

Novel View Synthesis

Distributed Variational Inference for Online Supervised Learning

1 code implementation5 Sep 2023 Parth Paritosh, Nikolay Atanasov, Sonia Martinez

Our key contribution lies in the derivation of a separable lower bound on the centralized estimation objective, which enables distributed variational inference with one-hop communication in a sensor network.

Binary Classification Variational Inference

Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks

no code implementations10 Jul 2023 Eduardo Sebastian, Thai Duong, Nikolay Atanasov, Eduardo Montijano, Carlos Sagues

The graph identification problem consists of discovering the interactions among nodes in a network given their state/feature trajectories.

Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories

1 code implementation3 Dec 2022 Pengzhi Yang, Shumon Koga, Arash Asgharivaskasi, Nikolay Atanasov

This paper proposes a novel model-based policy gradient algorithm for tracking dynamic targets using a mobile robot, equipped with an onboard sensor with limited field of view.

Continuous Control

Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems

1 code implementation29 Nov 2022 Valentin Duruisseaux, Thai Duong, Melvin Leok, Nikolay Atanasov

In this paper, we introduce a new structure-preserving deep learning architecture, the Lie group Forced Variational Integrator Network (LieFVIN), capable of learning controlled Lagrangian or Hamiltonian dynamics on Lie groups, either from position-velocity or position-only data.

Computational Efficiency Position

Learning Continuous Control Policies for Information-Theoretic Active Perception

1 code implementation26 Sep 2022 Pengzhi Yang, YuHan Liu, Shumon Koga, Arash Asgharivaskasi, Nikolay Atanasov

This paper proposes a method for learning continuous control policies for active landmark localization and exploration using an information-theoretic cost.

Continuous Control

LEMURS: Learning Distributed Multi-Robot Interactions

1 code implementation20 Sep 2022 Eduardo Sebastian, Thai Duong, Nikolay Atanasov, Eduardo Montijano, Carlos Sagues

This paper presents LEMURS, an algorithm for learning scalable multi-robot control policies from cooperative task demonstrations.

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).

Decoder 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 +1

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.

Object

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

1 code implementation24 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.

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.

Object Retrieval

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.

Object

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 Object

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 +1

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 +3

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

4 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.

Object

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 +1

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

2 code implementations5 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.

Vocal Bursts Intensity Prediction

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.

Model Predictive Control

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 +4

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 +2

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 +3

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