1 code implementation • 20 Mar 2022 • Eric Heiden, Ziang Liu, Vibhav Vineet, Erwin Coumans, Gaurav S. Sukhatme
Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains where real-world interaction or labeled data are difficult to obtain.
1 code implementation • 27 Feb 2022 • Xiaofeng Gao, Qiaozi Gao, Ran Gong, Kaixiang Lin, Govind Thattai, Gaurav S. Sukhatme
Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural language command to the agent, and the agent can only follow the command passively.
no code implementations • 15 Feb 2022 • Cristian-Paul Bara, Qing Ping, Abhinav Mathur, Govind Thattai, Rohith MV, Gaurav S. Sukhatme
We introduce a novel privacy-preserving methodology for performing Visual Question Answering on the edge.
1 code implementation • 11 Nov 2021 • I-Chun Arthur Liu, Shagun Uppal, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert, Youngwoon Lee
Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations.
1 code implementation • 10 Nov 2021 • Yizhou Zhao, Kaixiang Lin, Zhiwei Jia, Qiaozi Gao, Govind Thattai, Jesse Thomason, Gaurav S. Sukhatme
However, current simulators for Embodied AI (EAI) challenges only provide simulated indoor scenes with a limited number of layouts.
no code implementations • 19 Oct 2021 • K. R. Zentner, Ryan Julian, Ujjwal Puri, Yulun Zhang, Gaurav S. Sukhatme
We take a fresh look at this problem, by considering a setting in which the robot is limited to storing that knowledge and experience only in the form of learned skill policies.
no code implementations • 25 Sep 2021 • Amanda Prorok, Matthew Malencia, Luca Carlone, Gaurav S. Sukhatme, Brian M. Sadler, Vijay Kumar
In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by leveraging system-wide complementarity, diversity, and redundancy - often involving a reconfiguration of robotic capabilities to provide some key ability that was not present in the system a priori.
1 code implementation • 23 Sep 2021 • Gautam Salhotra, Christopher E. Denniston, David A. Caron, Gaurav S. Sukhatme
We find that by using knowledge of the number of rollouts allocated, the agent can more effectively choose actions to explore.
no code implementations • 18 Sep 2021 • Eric Heiden, Christopher E. Denniston, David Millard, Fabio Ramos, Gaurav S. Sukhatme
We address the latter problem of estimating parameters through a Bayesian inference approach that approximates a posterior distribution over simulation parameters given real sensor measurements.
2 code implementations • 9 Nov 2020 • Eric Heiden, David Millard, Erwin Coumans, Yizhou Sheng, Gaurav S. Sukhatme
Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation parameters that best fit the observed sensor readings.
Robotics
no code implementations • 22 Oct 2020 • Jun Yamada, Youngwoon Lee, Gautam Salhotra, Karl Pertsch, Max Pflueger, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert
In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment.
1 code implementation • 24 Sep 2020 • Giovanni Sutanto, Isabel M. Rayas Fernández, Peter Englert, Ragesh K. Ramachandran, Gaurav S. Sukhatme
Constrained robot motion planning is a widely used technique to solve complex robot tasks.
1 code implementation • 12 Jul 2020 • Eric Heiden, David Millard, Erwin Coumans, Gaurav S. Sukhatme
We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the computation.
no code implementations • 29 Jun 2020 • Ali-akbar Agha-mohammadi, Eric Heiden, Karol Hausman, Gaurav S. Sukhatme
Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments.
1 code implementation • 13 Jun 2020 • Isabel M. Rayas Fernández, Giovanni Sutanto, Peter Englert, Ragesh K. Ramachandran, Gaurav S. Sukhatme
Motion planning with constraints is an important part of many real-world robotic systems.
Robotics Computational Geometry
no code implementations • ICML Workshop LifelongML 2020 • Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman
One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments, but most robot learning systems today are deployed as fixed policies which do not adapt after deployment.
1 code implementation • 3 Jun 2020 • Peter Englert, Isabel M. Rayas Fernández, Ragesh K. Ramachandran, Gaurav S. Sukhatme
We address the problem of planning robot motions in constrained configuration spaces where the constraints change throughout the motion.
Robotics Computational Geometry
no code implementations • 30 Apr 2020 • Max Pflueger, Gaurav S. Sukhatme
We show how these embedding spaces can then be used as an augmentation to the robot state in reinforcement learning problems.
no code implementations • 21 Apr 2020 • Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman
One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments.
no code implementations • 24 Mar 2020 • Pradipta Ghosh, Xiaochen Liu, Hang Qiu, Marcos A. M. Vieira, Gaurav S. Sukhatme, Ramesh Govindan
Public cameras often have limited metadata describing their attributes.
1 code implementation • 7 Mar 2020 • Eric Heiden, Luigi Palmieri, Kai O. Arras, Gaurav S. Sukhatme, Sven Koenig
Planning smooth and energy-efficient motions for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics.
1 code implementation • L4DC 2020 • Giovanni Sutanto, Austin S. Wang, Yixin Lin, Mustafa Mukadam, Gaurav S. Sukhatme, Akshara Rai, Franziska Meier
The recursive Newton-Euler Algorithm (RNEA) is a popular technique for computing the dynamics of robots.
Robotics
no code implementations • 22 Jan 2020 • David Millard, Eric Heiden, Shubham Agrawal, Gaurav S. Sukhatme
A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the effects of their actions in a dynamic environment.
no code implementations • 3 Dec 2019 • Eric Heiden, Ziang Liu, Ragesh K. Ramachandran, Gaurav S. Sukhatme
Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurements of the environment.
no code implementations • 3 Oct 2019 • Ragesh K. Ramachandran, Lifeng Zhou James A. Preiss, Gaurav S. Sukhatme
We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest.
2 code implementations • 26 May 2019 • Eric Heiden, David Millard, Hejia Zhang, Gaurav S. Sukhatme
While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables.
2 code implementations • 11 Mar 2019 • Artem Molchanov, Tao Chen, Wolfgang Hönig, James A. Preiss, Nora Ayanian, Gaurav S. Sukhatme
Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation.
Robotics
no code implementations • 29 Sep 2018 • Hejia Zhang, Eric Heiden, Stefanos Nikolaidis, Joseph J. Lim, Gaurav S. Sukhatme
Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives must be met and the high-level context be taken into consideration.
no code implementations • ICLR 2018 • Max Pflueger, Ali Agha, Gaurav S. Sukhatme
In order to deal with complex terrain observations and policy learning, we propose a value iteration recurrence, referred to as the soft value iteration network (SVIN).
no code implementations • 24 Nov 2016 • Zhibei Ma, Kai Yin, Lantao Liu, Gaurav S. Sukhatme
Different from most existing works where the profits are assumed to be static, in this work we investigate a variant that has arbitrary time-dependent profits.
no code implementations • 24 Sep 2016 • Kai-Chieh Ma, Lantao Liu, Gaurav S. Sukhatme
A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed.
no code implementations • 3 May 2016 • Lantao Liu, Gaurav S. Sukhatme
We consider a decision-making problem where the environment varies both in space and time.