no code implementations • 25 Oct 2024 • Maithili Patel, Sonia Chernova
TAACo can learn to predict the user's preferred manner of assistance with any given task, by mediating reasoning through a representation composed of abstract concepts built based on user feedback.
1 code implementation • 28 Nov 2022 • Maithili Patel, Sonia Chernova
Proactive robot assistance enables a robot to anticipate and provide for a user's needs without being explicitly asked.
no code implementations • 8 Nov 2022 • Weiyu Liu, Yilun Du, Tucker Hermans, Sonia Chernova, Chris Paxton
StructDiffusion even improves the success rate of assembling physically-valid structures out of unseen objects by on average 16% over an existing multi-modal transformer model trained on specific structures.
1 code implementation • 4 May 2022 • Angel Daruna, Devleena Das, Sonia Chernova
Results from our algorithmic evaluation affirm our model design choices, and the results of our user studies with non-experts support the need for the proposed inference reconciliation framework.
no code implementations • 21 Apr 2022 • Evana Gizzi, Lakshmi Nair, Sonia Chernova, Jivko Sinapov
Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems.
no code implementations • 11 Jan 2022 • Devleena Das, Been Kim, Sonia Chernova
Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task.
no code implementations • 6 Jan 2022 • John Seon Keun Yi, Yoonwoo Kim, Sonia Chernova
This paper aims to disambiguate the human's referring expressions by allowing the agent to ask relevant questions based on semantic data obtained from scene graphs.
no code implementations • 8 Aug 2021 • Devleena Das, Sonia Chernova
Our framework autonomously captures the semantic information in a scene to produce semantically descriptive explanations for everyday users.
no code implementations • 20 May 2021 • Devleena Das, Yasutaka Nishimura, Rajan P. Vivek, Naoto Takeda, Sean T. Fish, Thomas Ploetz, Sonia Chernova
In this work, we build on insights from Explainable Artificial Intelligence (XAI) techniques and introduce an explainable activity recognition framework in which we leverage leading XAI methods to generate natural language explanations that explain what about an activity led to the given classification.
no code implementations • 10 May 2021 • Angel Daruna, Lakshmi Nair, Weiyu Liu, Sonia Chernova
We validated the approach on a physical platform, which resulted in the successful generalization of initial task plans to 38 of 50 execution environments with errors resulting from autonomous robot operation included.
1 code implementation • 14 Jan 2021 • Angel Daruna, Mehul Gupta, Mohan Sridharan, Sonia Chernova
In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications.
no code implementations • 5 Jan 2021 • Devleena Das, Siddhartha Banerjee, Sonia Chernova
In order for error explanations to be meaningful, we investigate what types of information within a set of hand-scripted explanations are most helpful to non-experts for failure and solution identification.
no code implementations • 24 Dec 2020 • M. Asif Rana, Anqi Li, Dieter Fox, Sonia Chernova, Byron Boots, Nathan Ratliff
The policy structure provides the user an interface to 1) specifying the spaces that are directly relevant to the completion of the tasks, and 2) designing policies for certain tasks that do not need to be learned.
no code implementations • 24 Nov 2020 • Joanne Truong, Sonia Chernova, Dhruv Batra
Simulation offers the ability to train large numbers of robots in parallel, and offers an abundance of data.
Domain Adaptation PointGoal Navigation Robotics
no code implementations • 18 Nov 2020 • Devleena Das, Siddhartha Banerjee, Sonia Chernova
With the growing capabilities of intelligent systems, the integration of artificial intelligence (AI) and robots in everyday life is increasing.
1 code implementation • 12 Nov 2020 • Adithyavairavan Murali, Weiyu Liu, Kenneth Marino, Sonia Chernova, Abhinav Gupta
This is largely due to the scale of the datasets both in terms of the number of objects and tasks studied.
no code implementations • 3 Nov 2020 • Dhruv Batra, Angel X. Chang, Sonia Chernova, Andrew J. Davison, Jia Deng, Vladlen Koltun, Sergey Levine, Jitendra Malik, Igor Mordatch, Roozbeh Mottaghi, Manolis Savva, Hao Su
In the rearrangement task, the goal is to bring a given physical environment into a specified state.
1 code implementation • 2 Apr 2020 • Zackory Erickson, Eliot Xing, Bharat Srirangam, Sonia Chernova, Charles C. Kemp
Finally, we present how a robot can combine this high resolution local sensing with images from the robot's head-mounted camera to achieve accurate material classification over a scene of objects on a table.
no code implementations • 11 Feb 2020 • Devleena Das, Sonia Chernova
Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans.
BIG-bench Machine Learning Explainable Artificial Intelligence (XAI) +1
4 code implementations • 13 Dec 2019 • Abhishek Kadian, Joanne Truong, Aaron Gokaslan, Alexander Clegg, Erik Wijmans, Stefan Lee, Manolis Savva, Sonia Chernova, Dhruv Batra
Second, we investigate the sim2real predictivity of Habitat-Sim for PointGoal navigation.
no code implementations • 11 Nov 2019 • Nithin Shrivatsav, Lakshmi Nair, Sonia Chernova
This paper explores the problem of tool substitution, namely, identifying substitute tools for performing a task from a given set of candidate tools.
1 code implementation • 24 Sep 2019 • Weiyu Liu, Angel Daruna, Sonia Chernova
Semantic grasping is the problem of selecting stable grasps that are functionally suitable for specific object manipulation tasks.
Robotics
no code implementations • 1 Jul 2019 • Kalesha Bullard, Yannick Schroecker, Sonia Chernova
Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives.
no code implementations • 29 May 2019 • Angel Daruna, Weiyu Liu, Zsolt Kira, Sonia Chernova
Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution.
1 code implementation • 26 May 2019 • Weiyu Liu, Angel Daruna, Zsolt Kira, Sonia Chernova
The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base.
no code implementations • 24 Mar 2019 • Angel Daruna, Weiyu Liu, Zsolt Kira, Sonia Chernova
Autonomous service robots require computational frameworks that allow them to generalize knowledge to new situations in a manner that models uncertainty while scaling to real-world problem sizes.
no code implementations • 11 Sep 2018 • Jonathan C Balloch, Varun Agrawal, Irfan Essa, Sonia Chernova
We show that pretraining real-time segmentation architectures with synthetic segmentation data instead of ImageNet improves fine-tuning performance by reducing the bias learned in pretraining and closing the \textit{transfer gap} as a result.
2 code implementations • 10 May 2018 • Zackory Erickson, Nathan Luskey, Sonia Chernova, Charles C. Kemp
To explore this, we collected a dataset of spectral measurements from two commercially available spectrometers during which a robotic platform interacted with 50 flat material objects, and we show that a neural network model can accurately analyze these measurements.
no code implementations • 26 Apr 2018 • Lakshmi Nair, Sonia Chernova
This paper explores the problem of task learning and planning, contributing the Action-Category Representation (ACR) to improve computational performance of both Planning and Reinforcement Learning (RL).
1 code implementation • 10 Jul 2017 • Zackory Erickson, Sonia Chernova, Charles C. Kemp
Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled.
no code implementations • 1 Jul 2016 • Haley Garrison, Sonia Chernova
This paper details the implementation of an algorithm for automatically generating a high-level knowledge network to perform commonsense reasoning, specifically with the application of robotic task repair.
no code implementations • 15 Jan 2014 • Sonia Chernova, Manuela Veloso
We present Confidence-Based Autonomy (CBA), an interactive algorithm for policy learning from demonstration.