Search Results for author: Stefanie Tellex

Found 22 papers, 11 papers with code

Where were my keys? -- Aggregating Spatial-Temporal Instances of Objects for Efficient Retrieval over Long Periods of Time

no code implementations25 Oct 2021 Ifrah Idrees, Zahid Hasan, Steven P. Reiss, Stefanie Tellex

Robots equipped with situational awareness can help humans efficiently find their lost objects by leveraging spatial and temporal structure.

Image Retrieval

Towards Optimal Correlational Object Search

1 code implementation19 Oct 2021 Kaiyu Zheng, Rohan Chitnis, Yoonchang Sung, George Konidaris, Stefanie Tellex

In realistic applications of object search, robots will need to locate target objects in complex environments while coping with unreliable sensors, especially for small or hard-to-detect objects.

Generalizing to New Domains by Mapping Natural Language to Lifted LTL

no code implementations11 Oct 2021 Eric Hsiung, Hiloni Mehta, Junchi Chu, Xinyu Liu, Roma Patel, Stefanie Tellex, George Konidaris

We compare our method of mapping natural language task specifications to intermediate contextual queries against state-of-the-art CopyNet models capable of translating natural language to LTL, by evaluating whether correct LTL for manipulation and navigation task specifications can be output, and show that our method outperforms the CopyNet model on unseen object references.

Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings

no code implementations28 Jul 2021 Sreehari Rammohan, Shangqun Yu, Bowen He, Eric Hsiung, Eric Rosen, Stefanie Tellex, George Konidaris

Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain accurate optimal value and policy estimates.

Continuous Control Data Augmentation +3

Dialogue Object Search

no code implementations22 Jul 2021 Monica Roy, Kaiyu Zheng, Jason Liu, Stefanie Tellex

To this end, we introduce a new task, dialogue object search: A robot is tasked to search for a target object (e. g. fork) in a human environment (e. g., kitchen), while engaging in a "video call" with a remote human who has additional but inexact knowledge about the target's location.

Bootstrapping Motor Skill Learning with Motion Planning

no code implementations12 Jan 2021 Ben Abbatematteo, Eric Rosen, Stefanie Tellex, George Konidaris

We propose using kinematic motion planning as a completely autonomous, sample efficient way to bootstrap motor skill learning for object manipulation.

Motion Planning

Spatial Language Understanding for Object Search in Partially Observed City-scale Environments

1 code implementation4 Dec 2020 Kaiyu Zheng, Deniz Bayazit, Rebecca Mathew, Ellie Pavlick, Stefanie Tellex

We propose SLOOP (Spatial Language Object-Oriented POMDP), a new framework for partially observable decision making with a probabilistic observation model for spatial language.

Decision Making

Task Scoping: Generating Task-Specific Abstractions for Planning

no code implementations17 Oct 2020 Nishanth Kumar, Michael Fishman, Natasha Danas, Michael Littman, Stefanie Tellex, George Konidaris

A generally intelligent agent requires an open-scope world model: one rich enough to tackle any of the wide range of tasks it may be asked to solve over its operational lifetime.

Generating Handwriting via Decoupled Style Descriptors

1 code implementation ECCV 2020 Atsunobu Kotani, Stefanie Tellex, James Tompkin

Instead, we introduce the Decoupled Style Descriptor (DSD) model for handwriting, which factors both character- and writer-level styles and allows our model to represent an overall greater space of styles.

Robot Object Retrieval with Contextual Natural Language Queries

1 code implementation23 Jun 2020 Thao Nguyen, Nakul Gopalan, Roma Patel, Matt Corsaro, Ellie Pavlick, Stefanie Tellex

The model takes in a language command containing a verb, for example "Hand me something to cut," and RGB images of candidate objects and selects the object that best satisfies the task specified by the verb.

Multi-Resolution POMDP Planning for Multi-Object Search in 3D

1 code implementation6 May 2020 Kaiyu Zheng, Yoonchang Sung, George Konidaris, Stefanie Tellex

Robots operating in households must find objects on shelves, under tables, and in cupboards.

pomdp_py: A Framework to Build and Solve POMDP Problems

1 code implementation21 Apr 2020 Kaiyu Zheng, Stefanie Tellex

In this paper, we present pomdp_py, a general purpose Partially Observable Markov Decision Process (POMDP) library written in Python and Cython.

Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control

2 code implementations23 Oct 2019 Jonathan Chang, Nishanth Kumar, Sean Hastings, Aaron Gokaslan, Diego Romeres, Devesh Jha, Daniel Nikovski, George Konidaris, Stefanie Tellex

We demonstrate that our model trained on 33% of the possible goals is able to generalize to more than 90% of the targets in the scene for both simulation and robot experiments.

Grounding Language Attributes to Objects using Bayesian Eigenobjects

no code implementations30 May 2019 Vanya Cohen, Benjamin Burchfiel, Thao Nguyen, Nakul Gopalan, Stefanie Tellex, George Konidaris

Our system is able to disambiguate between novel objects, observed via depth images, based on natural language descriptions.

3D Shape Representation

Planning with State Abstractions for Non-Markovian Task Specifications

1 code implementation28 May 2019 Yoonseon Oh, Roma Patel, Thao Nguyen, Baichuan Huang, Ellie Pavlick, Stefanie Tellex

Often times, we specify tasks for a robot using temporal language that can also span different levels of abstraction.

Implementing the Deep Q-Network

1 code implementation20 Nov 2017 Melrose Roderick, James Macglashan, Stefanie Tellex

The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and building point for much deep reinforcement learning research.

Atari Games reinforcement-learning

Deep Abstract Q-Networks

no code implementations2 Oct 2017 Melrose Roderick, Christopher Grimm, Stefanie Tellex

We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards.

Montezuma's Revenge reinforcement-learning

Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning

no code implementations31 Jul 2017 Lucas Lehnert, Stefanie Tellex, Michael L. Littman

One question central to Reinforcement Learning is how to learn a feature representation that supports algorithm scaling and re-use of learned information from different tasks.


Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities

1 code implementation21 Apr 2017 Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex

In this work, by grounding commands to all the tasks or subtasks available in a hierarchical planning framework, we arrive at a model capable of interpreting language at multiple levels of specificity ranging from coarse to more granular.

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