no code implementations • 12 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.
no code implementations • 2 Dec 2023 • Parth Paritosh, Nikolay Atanasov, Sonia Martinez
In this paper, we aim to design and analyze distributed Bayesian estimation algorithms for sensor networks.
no code implementations • 11 Sep 2023 • Binghao Huang, Yuanpei Chen, Tianyu Wang, Yuzhe Qin, Yaodong Yang, Nikolay Atanasov, Xiaolong Wang
Humans throw and catch objects all the time.
1 code implementation • 5 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.
no code implementations • 10 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.
1 code implementation • 3 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.
1 code implementation • 29 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.
1 code implementation • 26 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.
1 code implementation • 20 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.
no code implementations • 22 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).
no code implementations • 23 Apr 2022 • Qiaojun Feng, Nikolay Atanasov
A local mesh is reconstructed using an initialization and refinement stage.
1 code implementation • 18 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
no code implementations • 21 Sep 2021 • Thai Duong, Nikolay Atanasov
Adaptive control is a critical component of reliable robot autonomy in rapidly changing operational conditions.
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.
no code implementations • 23 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.
1 code implementation • 24 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.
no code implementations • 11 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.
no code implementations • 10 Mar 2021 • Tianyu Wang, Nikolay Atanasov
This paper presents a method for learning logical task specifications and cost functions from demonstrations.
no code implementations • 8 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.
no code implementations • 8 Mar 2021 • Qiaojun Feng, Nikolay Atanasov
This paper focuses on pose registration of different object instances from the same category.
no code implementations • 7 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
1 code implementation • 6 Jan 2021 • Qiaojun Feng, Nikolay Atanasov
Each local mesh is initialized from sparse depth measurements.
no code implementations • 1 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.
1 code implementation • 29 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.
no code implementations • 3 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
1 code implementation • 15 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
4 code implementations • 29 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.
no code implementations • 9 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.
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.
no code implementations • 14 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
no code implementations • 26 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.
2 code implementations • 5 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.
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.
no code implementations • 23 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.
2 code implementations • 7 Oct 2018 • Sikang Liu, Kartik Mohta, Nikolay Atanasov, Vijay Kumar
Search-based motion planning has been used for mobile robots in many applications.
Robotics
no code implementations • 23 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
no code implementations • 6 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
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.
no code implementations • 7 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.
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.
no code implementations • 23 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.
no code implementations • 1 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.
no code implementations • 20 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.