Search Results for author: Mohan Sridharan

Found 21 papers, 1 papers with code

Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration

no code implementations4 Apr 2024 Shivam Singh, Karthik Swaminathan, Raghav Arora, Ramandeep Singh, Ahana Datta, Dipanjan Das, Snehasis Banerjee, Mohan Sridharan, Madhava Krishna

Specifically, DaTAPlan planner computes actions for an agent and a human to collaboratively and jointly achieve the tasks anticipated by the LLM, and the agent automatically adapts to unexpected changes in human action outcomes and preferences.

Relevance Score: A Landmark-Like Heuristic for Planning

no code implementations12 Mar 2024 Oliver Kim, Mohan Sridharan

We describe an approach to compute this relevance score and use it as a heuristic in the search for a plan.


Knowledge-based Reasoning and Learning under Partial Observability in Ad Hoc Teamwork

no code implementations1 Jun 2023 Hasra Dodampegama, Mohan Sridharan

They use a large labeled dataset of prior observations to model the behavior of other agent types and to determine the ad hoc agent's behavior.

Logical Reasoning

Sequence-Agnostic Multi-Object Navigation

no code implementations10 May 2023 Nandiraju Gireesh, Ayush Agrawal, Ahana Datta, Snehasis Banerjee, Mohan Sridharan, Brojeshwar Bhowmick, Madhava Krishna

The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes.


Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation

no code implementations27 Aug 2022 D. A. Sasi Kiran, Kritika Anand, Chaitanya Kharyal, Gulshan Kumar, Nandiraju Gireesh, Snehasis Banerjee, Ruddra dev Roychoudhury, Mohan Sridharan, Brojeshwar Bhowmick, Madhava Krishna

This paper describes a framework for the object-goal navigation task, which requires a robot to find and move to the closest instance of a target object class from a random starting position.

Bayesian Inference Object +2

Object Goal Navigation using Data Regularized Q-Learning

no code implementations27 Aug 2022 Nandiraju Gireesh, D. A. Sasi Kiran, Snehasis Banerjee, Mohan Sridharan, Brojeshwar Bhowmick, Madhava Krishna

Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal ('where to go') based on the semantic map to locate the target object instance.

Data Augmentation Navigate +2

Knowledge-based and Data-driven Reasoning and Learning for Ad Hoc Teamwork

no code implementations24 Aug 2022 Hasra Dodampegama, Mohan Sridharan

We present an architecture for ad hoc teamwork, which refers to collaboration in a team of agents without prior coordination.

Decision Making Incremental Learning +1

A Survey of Ad Hoc Teamwork Research

no code implementations16 Feb 2022 Reuth Mirsky, Ignacio Carlucho, Arrasy Rahman, Elliot Fosong, William Macke, Mohan Sridharan, Peter Stone, Stefano V. Albrecht

Ad hoc teamwork is the research problem of designing agents that can collaborate with new teammates without prior coordination.

Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics

no code implementations25 Jan 2022 Mohan Sridharan, Tiago Mota

Algorithms based on deep network models are being used for many pattern recognition and decision-making tasks in robotics and AI.

Decision Making Logical Reasoning

The Ninth Advances in Cognitive Systems (ACS) Conference

no code implementations16 Jan 2022 Mark Burstein, Mohan Sridharan, David McDonald

ACS is an annual meeting for research on the initial goals of artificial intelligence and cognitive science, which aimed to explain the mind in computational terms and to reproduce the entire range of human cognitive abilities in computational artifacts.

Towards a Framework for Changing-Contact Robot Manipulation

no code implementations21 Jun 2021 Saif Sidhik, Mohan Sridharan, Dirk Ruiken

We present a framework for smooth dynamics and control of such changing-contact manipulation tasks.

Robot Manipulation

Continual Learning of Knowledge Graph Embeddings

1 code implementation14 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.

Continual Learning Knowledge Graph Embedding +2

Axiom Learning and Belief Tracing for Transparent Decision Making in Robotics

no code implementations20 Oct 2020 Tiago Mota, Mohan Sridharan

A robot's ability to provide descriptions of its decisions and beliefs promotes effective collaboration with humans.

Decision Making Logical Reasoning +1

A Survey of Knowledge-based Sequential Decision Making under Uncertainty

no code implementations19 Aug 2020 Shiqi Zhang, Mohan Sridharan

Reasoning with declarative knowledge (RDK) and sequential decision-making (SDM) are two key research areas in artificial intelligence.

Decision Making Decision Making Under Uncertainty +2

Non-monotonic Logical Reasoning Guiding Deep Learning for Explainable Visual Question Answering

no code implementations23 Sep 2019 Heather Riley, Mohan Sridharan

In the context of answering explanatory questions about scenes and the underlying classification problems, the architecture uses deep networks for extracting features from images and for generating answers to queries.

Logical Reasoning Question Answering +1

Towards a Theory of Intentions for Human-Robot Collaboration

no code implementations31 Jul 2019 Rocio Gomez, Mohan Sridharan, Heather Riley

Each abstract action is implemented as a sequence of concrete actions by automatically zooming to and reasoning with the part of the fine-resolution transition diagram relevant to the current coarse-resolution transition and the goal.

Computational Efficiency Logical Reasoning

Generative grasp synthesis from demonstration using parametric mixtures

no code implementations27 Jun 2019 Ermano Arruda, Claudio Zito, Mohan Sridharan, Marek Kopicki, Jeremy L. Wyatt

We present a parametric formulation for learning generative models for grasp synthesis from a demonstration.


REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

no code implementations17 Aug 2015 Mohan Sridharan, Michael Gelfond, Shiqi Zhang, Jeremy Wyatt

This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge.

Mixed Logical and Probabilistic Reasoning for Planning and Explanation Generation in Robotics

no code implementations1 Aug 2015 Zenon Colaco, Mohan Sridharan

The architecture described in this paper couples the non-monotonic logical reasoning capabilities of a declarative language with probabilistic belief revision, enabling robots to represent and reason with qualitative and quantitative descriptions of knowledge and degrees of belief.

Explanation Generation Logical Reasoning

KR$^3$: An Architecture for Knowledge Representation and Reasoning in Robotics

no code implementations5 May 2014 Shiqi Zhang, Mohan Sridharan, Michael Gelfond, Jeremy Wyatt

This paper describes an architecture that combines the complementary strengths of declarative programming and probabilistic graphical models to enable robots to represent, reason with, and learn from, qualitative and quantitative descriptions of uncertainty and knowledge.

Combining Answer Set Programming and POMDPs for Knowledge Representation and Reasoning on Mobile Robots

no code implementations29 Jul 2013 Shiqi Zhang, Mohan Sridharan

For widespread deployment in domains characterized by partial observability, non-deterministic actions and unforeseen changes, robots need to adapt sensing, processing and interaction with humans to the tasks at hand.

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