AIConfPaper
← Search

Mridul Aanjaneya

6 accepted papers

2026

An Open-Source, Reproducible Tensegrity Robot That Can Navigate Among Obstacles

William R. Johnson III, Patrick Meng, Nelson Chen, Luca Cimatti, Augustin Vercoutere, Mridul Aanjaneya +2
RA-L 2026

Tensegrity robots, composed of rigid struts and elastic tendons, provide impact resistance, low mass, and adaptability to unstructured terrain. Their compliance and complex, coupled dynamics, however, present modeling and control challenges, hindering planning and obstacle avoidance. This letter pre

Cited by 3Source ↗Scholar ↗
2024

Learning Differentiable Tensegrity Dynamics using Graph Neural Networks

Nelson Chen, Kun Wang, William R. Johson III, Rebecca Kramer-Bottiglio, Kostas Bekris, Mridul Aanjaneya
CoRL 2024poster

Tensegrity robots are composed of rigid struts and flexible cables. They constitute an emerging class of hybrid rigid-soft robotic systems and are promising systems for a wide array of applications, ranging from locomotion to assembly. They are difficult to control and model accurately, however, due…

Cited by 0Source ↗code ↗Scholar ↗
2023

Real2Sim2Real Transfer for Control of Cable-Driven Robots Via a Differentiable Physics Engine

Kun Wang, William R. Johnson, Shiyang Lu, Xiaonan Huang, Joran Booth, Rebecca Kramer-Bottiglio +2
IROS 2023poster

Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and significant deformations, which enable them to navigate unstructured terrains and survive harsh impacts. They are hard to control, however, due to high dimensionality, complex dynamics, and a co…

Cited by 11Source ↗Scholar ↗
2022

A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data

Kun Wang, Mridul Aanjaneya, Kostas Bekris
ICRA 2022poster

Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differentiable physics engines have been recently proposed as a data-driven approach for model identification of such complex r…

Cited by 10Source ↗Scholar ↗
2022

Model Identification and Control of a Low-cost Mobile Robot with Omnidirectional Wheels using Differentiable Physics

Edgar Granados, Abdeslam Boularias, Kostas Bekris, Mridul Aanjaneya
ICRA 2022poster

We present a new data-driven technique for pre-dicting the motion of a low-cost omnidirectional mobile robot under the influence of motor torques and friction forces. Our method utilizes a novel differentiable physics engine for analytically computing the gradient of the deviation between predicted…

Cited by 7Source ↗Scholar ↗
2021

Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots

Kun Wang, Mridul Aanjaneya, Kostas Bekris
IROS 2021poster

Learning policies in simulation is promising for reducing human effort when training robot controllers. This is especially true for soft robots that are more adaptive and safe but also more difficult to accurately model and control. The sim2real gap is the main barrier to successfully transfer polic…

Cited by 25Source ↗Scholar ↗