activity
20132023
most citedA Survey of Knowledge-based Sequential Decision Making under Uncertainty

15 citations · 61 across the 23 of their papers we have counts for

collaborators
Showing cs.ROShow all

14 papers · 1 filter

cs.RO2023

CLIPGraphs: Multimodal Graph Networks to Infer Object-Room Affinities

Ayush Agrawal, Raghav Arora, Ahana Datta +5

This paper introduces a novel method for determining the best room to place an object in, for embodied scene rearrangement. While state-of-the-art approaches rely on large language…

cs.RO2023

Sequence-Agnostic Multi-Object Navigation

Nandiraju Gireesh, Ayush Agrawal, Ahana Datta +4

The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes. It is a fundamental task for an assistive robot in a home or…

cs.RO2023★ 13 cited

RAMP: A Benchmark for Evaluating Robotic Assembly Manipulation and Planning

Jack Collins, Mark Robson, Jun Yamada +3

We introduce RAMP, an open-source robotics benchmark inspired by real-world industrial assembly tasks. RAMP consists of beams that a robot must assemble into specified goal configu…

cs.RO2022★ 1 cited

Toward a Framework for Adaptive Impedance Control of an Upper-limb Prosthesis

Laura Ferrante, Mohan Sridharan, Claudio Zito +1

Adapting upper-limb impedance (i.e., stiffness, damping, inertia) is essential for humans interacting with dynamic environments for executing grasping or manipulation tasks. On the…

cs.RO2022★ 1 cited

Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation

D. A. Sasi Kiran, Kritika Anand, Chaitanya Kharyal +7

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 startin…

cs.RO2022★ 1 cited

Object Goal Navigation using Data Regularized Q-Learning

Nandiraju Gireesh, D. A. Sasi Kiran, Snehasis Banerjee +3

Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semant…

cs.RO2022★ 2 cited

BURG-Toolkit: Robot Grasping Experiments in Simulation and the Real World

Martin Rudorfer, Markus Suchi, Mohan Sridharan +2

This paper presents BURG-Toolkit, a set of open-source tools for Benchmarking and Understanding Robotic Grasping. Our tools allow researchers to: (1) create virtual scenes for gene…

cs.RO2022

Generating Task-specific Robotic Grasps

Mark Robson, Mohan Sridharan

This paper describes a method for generating robot grasps by jointly considering stability and other task and object-specific constraints. We introduce a three-level representation…

cs.RO2021

An Adaptive Framework for Reliable Trajectory Following in Changing-Contact Robot Manipulation Tasks

Saif Sidhik, Mohan Sridharan, Dirk Ruiken

We describe a framework for changing-contact robot manipulation tasks that require the robot to make and break contacts with objects and surfaces. The discontinuous interaction dyn…

cs.RO2021★ 1 cited

Towards a Framework for Changing-Contact Robot Manipulation

Saif Sidhik, Mohan Sridharan, Dirk Ruiken

Many robot manipulation tasks require the robot to make and break contact with objects and surfaces. The dynamics of such changing-contact robot manipulation tasks are discontinuou…

cs.RO2021

Continual Learning of Knowledge Graph Embeddings

Angel Daruna, Mehul Gupta, Mohan Sridharan +1

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. Ho…

cs.RO2019★ 4 cited

Generative grasp synthesis from demonstration using parametric mixtures

Ermano Arruda, Claudio Zito, Mohan Sridharan +2

We present a parametric formulation for learning generative models for grasp synthesis from a demonstration. We cast new light on this family of approaches, proposing a parametric…

cs.RO2015

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

Mohan Sridharan, Michael Gelfond, Shiqi Zhang +1

This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with…

cs.RO2015★ 1 cited

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

Zenon Colaco, Mohan Sridharan

Robots assisting humans in complex domains have to represent knowledge and reason at both the sensorimotor level and the social level. The architecture described in this paper coup…