Search Results for author: Andrew Szot

Found 12 papers, 5 papers with code

Skill Transformer: A Monolithic Policy for Mobile Manipulation

no code implementations ICCV 2023 Xiaoyu Huang, Dhruv Batra, Akshara Rai, Andrew Szot

We present Skill Transformer, an approach for solving long-horizon robotic tasks by combining conditional sequence modeling and skill modularity.

Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-Second

1 code implementation CVPR 2023 Vincent-Pierre Berges, Andrew Szot, Devendra Singh Chaplot, Aaron Gokaslan, Roozbeh Mottaghi, Dhruv Batra, Eric Undersander

Specifically, a Fetch robot (equipped with a mobile base, 7DoF arm, RGBD camera, egomotion, and onboard sensing) is spawned in a home environment and asked to rearrange objects - by navigating to an object, picking it up, navigating to a target location, and then placing the object at the target location.

Reinforcement Learning (RL)

Adaptive Coordination in Social Embodied Rearrangement

no code implementations31 May 2023 Andrew Szot, Unnat Jain, Dhruv Batra, Zsolt Kira, Ruta Desai, Akshara Rai

We present the task of "Social Rearrangement", consisting of cooperative everyday tasks like setting up the dinner table, tidying a house or unpacking groceries in a simulated multi-agent environment.

Housekeep: Tidying Virtual Households using Commonsense Reasoning

1 code implementation22 May 2022 Yash Kant, Arun Ramachandran, Sriram Yenamandra, Igor Gilitschenski, Dhruv Batra, Andrew Szot, Harsh Agrawal

Instead, the agent must learn from and is evaluated against human preferences of which objects belong where in a tidy house.

Language Modelling Large Language Model

Goal-Driven Imitation Learning from Observation by Inferring Goal Proximity

no code implementations1 Jan 2021 Andrew Szot, Youngwoon Lee, Shao-Hua Sun, Joseph J Lim

Humans can effectively learn to estimate how close they are to completing a desired task simply by watching others fulfill the task.

Imitation Learning

Generalization to New Actions in Reinforcement Learning

2 code implementations ICML 2020 Ayush Jain, Andrew Szot, Joseph J. Lim

A fundamental trait of intelligence is the ability to achieve goals in the face of novel circumstances, such as making decisions from new action choices.

reinforcement-learning Reinforcement Learning (RL)

Cannot find the paper you are looking for? You can Submit a new open access paper.