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Tirthankar Bandyopadhyay

15 accepted papers

2026

Deformable Cluster Manipulation Via Whole-Arm Policy Learning

Jayadeep Jacob, Wenzheng Zhang, Houston Warren, Paulo Vinicius Koerich Borges, Fabio Ramos, Tirthankar Bandyopadhyay
ICRA 2026poster

Manipulating clusters of deformable objects presents a substantial challenge with widespread applicability, but requires contact-rich whole-arm interactions. A potential solution must address the limited capacity for realistic model synthesis, high uncertainty in perception, and the lack of efficien…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Demonstrating Event-Triggered Investigation and Sample Collection for Human Scientists using Field Robots and Large Foundation Models

Tirthankar Bandyopadhyay, Fletcher Talbot, Callum Bennie, Hashini Senaratne, Xun Li, Brendan Tidd +17
RSS 2024poster

In this paper, we introduce a pioneering end-to-end system demonstrated on a team of robots and sensors, designed to augment scientific exploration and discovery for human scientists in remote or inaccessible environments. We demonstrate and analyse our system's capability in a mock-up test-bed scen…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

Gentle Manipulation of Tree Branches: A Contact-Aware Policy Learning Approach

Jay Jacob, Shizhe Cai, Paulo Vinicius Koerich Borges, Tirthankar Bandyopadhyay, Fabio Ramos
CoRL 2024poster

Learning to interact with deformable tree branches with minimal damage is challenging due to their intricate geometry and inscrutable dynamics. Furthermore, traditional vision-based modelling systems suffer from implicit occlusions in dense foliage, severely changing lighting conditions, and limited…

Cited by 3Source ↗Scholar ↗
2024

Learning to Simulate Tree-Branch Dynamics for Manipulation

Jayadeep Jacob, Tirthankar Bandyopadhyay, Jason Williams, Paulo V. K. Borges, Fabio Ramos
RA-L 2024

We propose to use a simulation driven inverse inference approach to model the dynamics of tree branches under manipulation. Learning branch dynamics and gaining the ability to manipulate deformable vegetation can help with occlusion-prone tasks, such as fruit picking in dense foliage, as well as mov

Cited by 9Source ↗Scholar ↗
2023

Sample Efficient Dynamics Learning for Symmetrical Legged Robots: Leveraging Physics Invariance and Geometric Symmetries

Jee-eun Lee, Jaemin Lee, Tirthankar Bandyopadhyay, Luis Sentis
ICRA 2023poster

Model generalization of the underlying dynamics is critical for achieving data efficiency when learning for robot control. This paper proposes a novel approach for learning dynamics leveraging the symmetry in the underlying robotic system, which allows for robust extrapolation from fewer samples. Ex…

Cited by 3Source ↗Scholar ↗
2021

PROMPT: Probabilistic Motion Primitives based Trajectory Planning

Tobias Loew, Tirthankar Bandyopadhyay, Jason Williams, Paulo Borges
RSS 2021poster

We present a novel approach to motion planning for autonomous ground vehicles by formulating motion primitives as probabilistic distributions of trajectories (aka probabilistic motion primitives - ProMP) and performing stochastic optimisation on them for finding an optimal path. We show that compar…

Cited by 18Source ↗PDF ↗Scholar ↗
2020

Identification of Effective Motion Primitives for Ground Vehicles

Tobias Löw, Tirthankar Bandyopadhyay, Paulo V. K. Borges
IROS 2020poster

Understanding the kinematics of a ground robot is essential for efficient navigation. Based on the kinematic model of a robot, its full motion capabilities can be represented by theoretical motion primitives. However, depending on the environment and/or human preferences, not all of those theoretica…

Cited by 8Source ↗Scholar ↗
2020

PaintPath: Defining Path Directionality in Maps for Autonomous Ground Vehicles

Riley Bowyer, Thomas Lowe, Paulo Borges, Tirthankar Bandyopadhyay, Tobias Löw, David Haddon
IROS 2020poster

Directionality in path planning is essential for efficient autonomous navigation in a number of real-world environments. In many map-based navigation scenarios, the viable path from a given point A to point B is not the same as the viable path from B to A. We present a method that automatically inco…

Cited by 0Source ↗Scholar ↗
2019

Dynamic Manipulation of Gear Ratio and Ride Height for a Novel Compliant Wheel using Pneumatic Actuators

Tim Hojnik, Paul Flick, Tirthankar Bandyopadhyay, Jonathan Roberts
ICRA 2019poster

This paper proposes a novel configurable wheel that exhibits desired properties of varied radius wheels. Positional manipulation of the centre hub is proposed and tested to achieve these desired characteristics of `virtual' wheels in a physical system. The centre hub is manipulated via the use of pn…

Cited by 2Source ↗Scholar ↗
2019

Walking Posture Adaptation for Legged Robot Navigation in Confined Spaces

Russell Buchanan, Tirthankar Bandyopadhyay, Marko Bjelonic, Lorenz Wellhausen, Marco Hutter, Navinda Kottege
RA-L 2019

Legged robots have the ability to adapt their walking posture to navigate confined spaces due to their high degrees of freedom. However, this has not been exploited in most common multilegged platforms. This letter presents a deformable bounding box abstraction of the robot model, with accompanying

Cited by 53Source ↗Scholar ↗
2018

Magneto: A Versatile Multi-Limbed Inspection Robot

Tirthankar Bandyopadhyay, Ryan Steindl, Fletcher Talbot, Navinda Kottege, Ross Dungavell, Brett Wood +3
IROS 2018poster

In this paper we present the design and control strategies of a novel quadruped climbing robot (named Magneto) with three degrees of freedom (3-DOF) actuated limbs and a 3-DOF compliant magnetic foot. By exploiting its high degrees of freedom, Magneto is able to deform its body shape to squeeze thro…

Cited by 77Source ↗Scholar ↗
2017

The Multilegged Autonomous eXplorer (MAX)

Alberto Elfes, Ryan Steindl, Fletcher Talbot, Farid Kendoul, Pavan Sikka, Tom Lowe +7
ICRA 2017poster

To address the goal of locomotion in very complex and difficult terrains, the authors are developing a new class of Ultralight Legged Robots. This paper presents the Multilegged Autonomous eXplorer (MAX), an ultralight, six-legged robot for traversal and exploration of challenging indoor and outdoor…

Cited by 28Source ↗Scholar ↗
2016

Differential jumping: A novel mode for micro-robot navigation

Tirthankar Bandyopadhyay, Karl von Richter, Marc-Antoine Pallaud, Alberto Elfes
ICRA 2016

Jumping is an effective navigation mode for micro-robots. Recent research has addressed significant challenges that exist in developing reliable, energy efficient, light weight micro actuation mechanisms, mostly focused on a single jump. However, there has been limited exploration of planning strate

Cited by 7Source ↗Scholar ↗
2015

Towards autonomous navigation of unsignalized intersections under uncertainty of human driver intent

Volkan Sezer, Tirthankar Bandyopadhyay, Daniela Rus, Emilio Frazzoli, David Hsu
IROS 2015poster

In a mixed environment of autonomous driverless vehicles and human driven vehicles operating on the same road, identifying intentions of human drivers and interacting with them in a compliant and responsible manner becomes a challenging problem for the driverless vehicles. In this paper, the problem…

Cited by 86Source ↗Scholar ↗