1 code implementation • 4 Apr 2024 • Lars Ankile, Anthony Simeonov, Idan Shenfeld, Pulkit Agrawal
While learning from demonstrations is powerful for acquiring visuomotor policies, high-performance imitation without large demonstration datasets remains challenging for tasks requiring precise, long-horizon manipulation.
no code implementations • 6 Mar 2024 • Marcel Torne, Anthony Simeonov, Zechu Li, April Chan, Tao Chen, Abhishek Gupta, Pulkit Agrawal
To learn performant, robust policies without the burden of unsafe real-world data collection or extensive human supervision, we propose RialTo, a system for robustifying real-world imitation learning policies via reinforcement learning in "digital twin" simulation environments constructed on the fly from small amounts of real-world data.
no code implementations • 25 Sep 2023 • Meenal Parakh, Alisha Fong, Anthony Simeonov, Tao Chen, Abhishek Gupta, Pulkit Agrawal
Large Language Models (LLMs) have been shown to act like planners that can decompose high-level instructions into a sequence of executable instructions.
no code implementations • 10 Jul 2023 • Anthony Simeonov, Ankit Goyal, Lucas Manuelli, Lin Yen-Chen, Alina Sarmiento, Alberto Rodriguez, Pulkit Agrawal, Dieter Fox
We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf.
no code implementations • 7 Feb 2023 • Ethan Chun, Yilun Du, Anthony Simeonov, Tomas Lozano-Perez, Leslie Kaelbling
A robot operating in a household environment will see a wide range of unique and unfamiliar objects.
1 code implementation • 17 Nov 2022 • Anthony Simeonov, Yilun Du, Lin Yen-Chen, Alberto Rodriguez, Leslie Pack Kaelbling, Tomas Lozano-Perez, Pulkit Agrawal
This formalism is implemented in three steps: assigning a consistent local coordinate frame to the task-relevant object parts, determining the location and orientation of this coordinate frame on unseen object instances, and executing an action that brings these frames into the desired alignment.
1 code implementation • 9 Dec 2021 • Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, Vincent Sitzmann
Our performance generalizes across both object instances and 6-DoF object poses, and significantly outperforms a recent baseline that relies on 2D descriptors.
no code implementations • 16 Nov 2020 • Anthony Simeonov, Yilun Du, Beomjoon Kim, Francois R. Hogan, Joshua Tenenbaum, Pulkit Agrawal, Alberto Rodriguez
We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i. e. without prior object models.
1 code implementation • 13 Jul 2019 • Ahmed H. Qureshi, Yinglong Miao, Anthony Simeonov, Michael C. Yip
We validate MPNet against gold-standard and state-of-the-art planning methods in a variety of problems from 2D to 7D robot configuration spaces in challenging and cluttered environments, with results showing significant and consistently stronger performance metrics, and motivating neural planning in general as a modern strategy for solving motion planning problems efficiently.
1 code implementation • 14 Jun 2018 • Ahmed H. Qureshi, Anthony Simeonov, Mayur J. Bency, Michael C. Yip
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars.