no code implementations • ICLR 2019 • Vikas Dhiman, Shurjo Banerjee, Jeffrey M. Siskind, Jason J. Corso
Multi-goal reinforcement learning (MGRL) addresses tasks where the desired goal state can change for every trial.
1 code implementation • 13 Mar 2024 • Soheil Gharatappeh, Sepideh Neshatfar, Salimeh Yasaei Sekeh, Vikas Dhiman
In this paper, we present a novel fog-aware object detection network called FogGuard, designed to address the challenges posed by foggy weather conditions.
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.
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 • 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.
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 • 4 Dec 2019 • Jing Bi, Vikas Dhiman, Tianyou Xiao, Chenliang Xu
The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer.
no code implementations • 25 Sep 2018 • Vikas Dhiman, Shurjo Banerjee, Jeffrey M. Siskind, Jason J. Corso
We do this by adapting the Floyd-Warshall algorithm for RL and call the adaptation Floyd-Warshall RL (FWRL).
1 code implementation • 7 Feb 2018 • Vikas Dhiman, Shurjo Banerjee, Brent Griffin, Jeffrey M. Siskind, Jason J. Corso
However, when trained and tested on different sets of maps, the algorithm fails to transfer the ability to gather and exploit map-information to unseen maps.
no code implementations • ICLR 2018 • Shurjo Banerjee, Vikas Dhiman, Brent Griffin, Jason J. Corso
As the title of the paper by Mirowski et al. (2016) suggests, one might assume that DRL-based algorithms are able to “learn to navigate” and are thus ready to replace classical mapping and path-planning algorithms, at least in simulated environments.
no code implementations • CVPR 2016 • Vikas Dhiman, Quoc-Huy Tran, Jason J. Corso, Manmohan Chandraker
We present a physically interpretable, continuous 3D model for handling occlusions with applications to road scene understanding.
no code implementations • 12 Apr 2016 • Suren Kumar, Vikas Dhiman, Madan Ravi Ganesh, Jason J. Corso
We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations.