no code implementations • 1 Mar 2024 • Noriaki Hirose, Dhruv Shah, Kyle Stachowicz, Ajay Sridhar, Sergey Levine
Specifically, SELFI stabilizes the online learning process by incorporating the same model-based learning objective from offline pre-training into the Q-values learned with online model-free reinforcement learning.
no code implementations • 17 Oct 2023 • Pavithra Harsha, Shivaram Subramanian, Ali Koc, Mahesh Ramakrishna, Brian Quanz, Dhruv Shah, Chandra Narayanaswami
Using a real-world dataset from a large American omnichannel retail chain, a business value assessment during a peak period indicates over a 15% profitability gain for BIO over RO and other baselines while also preserving the (practical) worst case performance.
no code implementations • 16 Oct 2023 • Dhruv Shah, Michael Equi, Blazej Osinski, Fei Xia, Brian Ichter, Sergey Levine
Navigation in unfamiliar environments presents a major challenge for robots: while mapping and planning techniques can be used to build up a representation of the world, quickly discovering a path to a desired goal in unfamiliar settings with such methods often requires lengthy mapping and exploration.
no code implementations • 11 Oct 2023 • Ajay Sridhar, Dhruv Shah, Catherine Glossop, Sergey Levine
In this paper, we describe how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration, with the latter providing the ability to search novel environments, and the former providing the ability to reach a user-specified goal once it has been located.
no code implementations • 26 Jun 2023 • Dhruv Shah, Ajay Sridhar, Nitish Dashora, Kyle Stachowicz, Kevin Black, Noriaki Hirose, Sergey Levine
In this paper, we describe the Visual Navigation Transformer (ViNT), a foundation model that aims to bring the success of general-purpose pre-trained models to vision-based robotic navigation.
no code implementations • 2 Jun 2023 • Noriaki Hirose, Dhruv Shah, Ajay Sridhar, Sergey Levine
By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space.
no code implementations • 12 May 2023 • Keyur D. Joshi, Dhruv Shah, Varshil Shah, Nilay Gandhi, Sanket J. Shah, Sanket B. Shah
Therefore, it was hypothesized that a digital image of a coin resting on its either size could be classified into its correct denomination by training a deep neural network model.
no code implementations • 19 Apr 2023 • Kyle Stachowicz, Dhruv Shah, Arjun Bhorkar, Ilya Kostrikov, Sergey Levine
We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL).
no code implementations • NeurIPS 2023 • Wenlong Huang, Fei Xia, Dhruv Shah, Danny Driess, Andy Zeng, Yao Lu, Pete Florence, Igor Mordatch, Sergey Levine, Karol Hausman, Brian Ichter
Recent progress in large language models (LLMs) has demonstrated the ability to learn and leverage Internet-scale knowledge through pre-training with autoregressive models.
1 code implementation • 16 Dec 2022 • Dhruv Shah, Arjun Bhorkar, Hrish Leen, Ilya Kostrikov, Nick Rhinehart, Sergey Levine
Reinforcement learning can enable robots to navigate to distant goals while optimizing user-specified reward functions, including preferences for following lanes, staying on paved paths, or avoiding freshly mowed grass.
no code implementations • 13 Dec 2022 • Sergey Levine, Dhruv Shah
Navigation is one of the most heavily studied problems in robotics, and is conventionally approached as a geometric mapping and planning problem.
no code implementations • 14 Oct 2022 • Noriaki Hirose, Dhruv Shah, Ajay Sridhar, Sergey Levine
Machine learning techniques rely on large and diverse datasets for generalization.
1 code implementation • 7 Oct 2022 • Dhruv Shah, Ajay Sridhar, Arjun Bhorkar, Noriaki Hirose, Sergey Levine
Learning provides a powerful tool for vision-based navigation, but the capabilities of learning-based policies are constrained by limited training data.
1 code implementation • 10 Jul 2022 • Dhruv Shah, Blazej Osinski, Brian Ichter, Sergey Levine
Goal-conditioned policies for robotic navigation can be trained on large, unannotated datasets, providing for good generalization to real-world settings.
no code implementations • 23 Feb 2022 • Dhruv Shah, Sergey Levine
In this work, we propose an approach that integrates learning and planning, and can utilize side information such as schematic roadmaps, satellite maps and GPS coordinates as a planning heuristic, without relying on them being accurate.
no code implementations • ICLR 2022 • Dhruv Shah, Peng Xu, Yao Lu, Ted Xiao, Alexander Toshev, Sergey Levine, Brian Ichter
Hierarchical reinforcement learning aims to enable this by providing a bank of low-level skills as action abstractions.
Hierarchical Reinforcement Learning reinforcement-learning +2
no code implementations • 12 Apr 2021 • Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine
We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments.
no code implementations • 17 Dec 2020 • Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine
We propose a learning-based navigation system for reaching visually indicated goals and demonstrate this system on a real mobile robot platform.
no code implementations • 17 Sep 2020 • Saumya Borwankar, Dhruv Shah
Free Space Optics (FSO) is a developing technology for Line of Sight communication that uses light propagation in free space that provides various advantages like high bandwidth, high data rate, ease of installation, free licensing and secure communication.
no code implementations • ICLR 2020 • Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine
The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.
no code implementations • 27 Apr 2020 • Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine
In this work, we discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world.
no code implementations • 23 Jan 2020 • Brian Quanz, Wei Sun, Ajay Deshpande, Dhruv Shah, Jae-Eun Park
We propose a flexible, co-creative framework bringing together multiple machine learning techniques to assist human users to efficiently produce effective creative designs.
no code implementations • 6 Feb 2019 • Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah
Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human brain.