no code implementations • 5 Feb 2024 • Jialu Li, Aishwarya Padmakumar, Gaurav Sukhatme, Mohit Bansal
Outdoor Vision-and-Language Navigation (VLN) requires an agent to navigate through realistic 3D outdoor environments based on natural language instructions.
no code implementations • 9 Aug 2023 • Hangjie Shi, Leslie Ball, Govind Thattai, Desheng Zhang, Lucy Hu, Qiaozi Gao, Suhaila Shakiah, Xiaofeng Gao, Aishwarya Padmakumar, Bofei Yang, Cadence Chung, Dinakar Guthy, Gaurav Sukhatme, Karthika Arumugam, Matthew Wen, Osman Ipek, Patrick Lange, Rohan Khanna, Shreyas Pansare, Vasu Sharma, Chao Zhang, Cris Flagg, Daniel Pressel, Lavina Vaz, Luke Dai, Prasoon Goyal, Sattvik Sahai, Shaohua Liu, Yao Lu, Anna Gottardi, Shui Hu, Yang Liu, Dilek Hakkani-Tur, Kate Bland, Heather Rocker, James Jeun, Yadunandana Rao, Michael Johnston, Akshaya Iyengar, Arindam Mandal, Prem Natarajan, Reza Ghanadan
The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge.
no code implementations • 7 Aug 2023 • Nirbhay Modhe, Qiaozi Gao, Ashwin Kalyan, Dhruv Batra, Govind Thattai, Gaurav Sukhatme
Offline reinforcement learning (RL) methods strike a balance between exploration and exploitation by conservative value estimation -- penalizing values of unseen states and actions.
no code implementations • 23 May 2023 • Sumeet Batra, Bryon Tjanaka, Matthew C. Fontaine, Aleksei Petrenko, Stefanos Nikolaidis, Gaurav Sukhatme
Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning.
no code implementations • 7 Apr 2023 • Gautam Salhotra, I-Chun Arthur Liu, Gaurav Sukhatme
Some Learning from Demonstrations (LfD) methods handle small mismatches in the action spaces of the teacher and student.
1 code implementation • NeurIPS 2023 • Qiaozi Gao, Govind Thattai, Suhaila Shakiah, Xiaofeng Gao, Shreyas Pansare, Vasu Sharma, Gaurav Sukhatme, Hangjie Shi, Bofei Yang, Desheng Zheng, Lucy Hu, Karthika Arumugam, Shui Hu, Matthew Wen, Dinakar Guthy, Cadence Chung, Rohan Khanna, Osman Ipek, Leslie Ball, Kate Bland, Heather Rocker, Yadunandana Rao, Michael Johnston, Reza Ghanadan, Arindam Mandal, Dilek Hakkani Tur, Prem Natarajan
We introduce Alexa Arena, a user-centric simulation platform for Embodied AI (EAI) research.
no code implementations • 12 Jan 2023 • Yuqian Jiang, Qiaozi Gao, Govind Thattai, Gaurav Sukhatme
For service robots to become general-purpose in everyday household environments, they need not only a large library of primitive skills, but also the ability to quickly learn novel tasks specified by users.
1 code implementation • 24 Jan 2022 • Zhiwei Jia, Kaixiang Lin, Yizhou Zhao, Qiaozi Gao, Govind Thattai, Gaurav Sukhatme
With the proposed Affordance-aware Multimodal Neural SLAM (AMSLAM) approach, we obtain more than 40% improvement over prior published work on the ALFRED benchmark and set a new state-of-the-art generalization performance at a success rate of 23. 48% on the test unseen scenes.
no code implementations • 28 Oct 2021 • Nicholas Roy, Ingmar Posner, Tim Barfoot, Philippe Beaudoin, Yoshua Bengio, Jeannette Bohg, Oliver Brock, Isabelle Depatie, Dieter Fox, Dan Koditschek, Tomas Lozano-Perez, Vikash Mansinghka, Christopher Pal, Blake Richards, Dorsa Sadigh, Stefan Schaal, Gaurav Sukhatme, Denis Therien, Marc Toussaint, Michiel Van de Panne
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains.
1 code implementation • 10 Aug 2021 • Alessandro Suglia, Qiaozi Gao, Jesse Thomason, Govind Thattai, Gaurav Sukhatme
Language-guided robots performing home and office tasks must navigate in and interact with the world.
no code implementations • 24 Jun 2021 • K. R. Zentner, Ryan Julian, Ujjwal Puri, Yulun Zhang, Gaurav Sukhatme
We explore possible methods for multi-task transfer learning which seek to exploit the shared physical structure of robotics tasks.
4 code implementations • ICML 2020 • Aleksei Petrenko, Zhehui Huang, Tushar Kumar, Gaurav Sukhatme, Vladlen Koltun
In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.
no code implementations • 25 Sep 2019 • Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, Franziska Meier
We present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures.
1 code implementation • 12 Jun 2019 • Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, Franziska Meier
This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time.
no code implementations • 4 Jan 2019 • Shoubhik Debnath, Gaurav Sukhatme, Lantao Liu
Then, we leverage this approximate model along with a notion of reachability using Mean First Passage Times to perform Model-Based reinforcement learning.
no code implementations • 4 Jan 2019 • Shoubhik Debnath, Lantao Liu, Gaurav Sukhatme
A new mechanism for efficiently solving the Markov decision processes (MDPs) is proposed in this paper.
no code implementations • 3 Jan 2019 • Shoubhik Debnath, Lantao Liu, Gaurav Sukhatme
The solution convergence of Markov Decision Processes (MDPs) can be accelerated by prioritized sweeping of states ranked by their potential impacts to other states.
1 code implementation • 4 Oct 2018 • Zhanpeng He, Ryan Julian, Eric Heiden, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav Sukhatme, Karol Hausman
We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot.
1 code implementation • 26 Sep 2018 • Ryan Julian, Eric Heiden, Zhanpeng He, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav Sukhatme, Karol Hausman
In particular, we first use simulation to jointly learn a policy for a set of low-level skills, and a "skill embedding" parameterization which can be used to compose them.
no code implementations • 4 Jul 2018 • Artem Molchanov, Karol Hausman, Stan Birchfield, Gaurav Sukhatme
In this work, we introduce a method based on region-growing that allows learning in an environment with any pair of initial and goal states.
no code implementations • NeurIPS 2017 • Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, Joseph Lim
Imitation learning has traditionally been applied to learn a single task from demonstrations thereof.
no code implementations • 13 Apr 2016 • Jeannette Bohg, Karol Hausman, Bharath Sankaran, Oliver Brock, Danica Kragic, Stefan Schaal, Gaurav Sukhatme
Recent approaches in robotics follow the insight that perception is facilitated by interaction with the environment.
Robotics
no code implementations • 9 Aug 2014 • Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan, Ali Oran, Patrick Jaillet, John Dolan, Gaurav Sukhatme
The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots.