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Swarun Kumar

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6 papers

2 author rows

ICRA Conference 2025 Conference Paper

Shape-Programming Robotic Reflectors for Wireless Networks

Yawen Liu
Akarsh Prabhakara
Jiangyifei Zhu
Shenyi Qiao
Swarun Kumar

With the increasing use of wireless technologies in robotics for communication, sensing, and localization, the potential benefits of how robotics can complement and enhance wireless systems remain underexplored. This paper explores a novel application of the existing inflatable robots for wireless communication systems by forming a shape-programming, reflective waveguide that enhances the received signal quality for wireless devices. Our primary target is enhancing Low-Power Wide-Area Networks (LP-WANs) - where 10-year batterypowered client devices (e. g. energy meters or smart home sensors) connect to cellular-like base stations to deliver data. Devices in these networks often experience significant seasonal variability in battery life - even simple obstructions between the device and base station (e. g. due to construction) can shave off years of battery life. We propose MetaMorph, a programmable robotic reflector attached to base stations that enhances signal quality from client devices by enhancing received signal energy with controlled reflections. We investigate the design of the reflector, and our experiments show the ability to improve the signal quality for LP-WAN (LoRa) communication systems demonstrating signal quality and battery-benefits. To our best knowledge, MetaMorph is the first paper to explore how flexible robotics can serve as virtuous reflectors for wireless communication systems.

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ICRA Conference 2023 Conference Paper

High Resolution Point Clouds from mmWave Radar

Akarsh Prabhakara
Tao Jin
Arnav Das 0001
Gantavya Bhatt
Lilly Kumari
Elahe Soltanaghai
Jeff A. Bilmes
Swarun Kumar

This paper explores a machine learning approach on data from a single-chip mmWave radar for generating high resolution point clouds – a key sensing primitive for robotic applications such as mapping, odometry and localization. Unlike lidar and vision-based systems, mmWave radar can operate in harsh environments and see through occlusions like smoke, fog, and dust. Unfortunately, current mmWave processing techniques offer poor spatial resolution compared to lidar point clouds. This paper presents RadarHD, an end-to-end neural network that constructs lidar-like point clouds from low resolution radar input. Enhancing radar images is challenging due to the presence of specular and spurious reflections. Radar data also doesn't map well to traditional image processing techniques due to the signal's sinc-like spreading pattern. We overcome these challenges by training RadarHD on a large volume of raw I/Q radar data paired with lidar point clouds across diverse indoor settings. Our experiments show the ability to generate rich point clouds even in scenes unobserved during training and in the presence of heavy smoke occlusion. Further, RadarHD's point clouds are high-quality enough to work with existing lidar odometry and mapping workflows.

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ICRA Conference 2023 Conference Paper

Navigating Soft Robots through Wireless Heating

Yiwen Song
Mason Zadan
Kushaan Misra
Zefang Li
Jingxian Wang
Carmel Majidi
Swarun Kumar

Recent work on battery-free soft robotics has demonstrated the use of liquid crystal elastomers (LCE) to build shape-changing materials activated by applied external heat. However, sources of heat must typically be in direct field-of-view of the robot (i. e. NIR, laser, and visual light EM sources or convective heats guns), be tethered to an external power supply (i. e. thermoelectric heating or resistive joule heaters), or require a heavy on-board battery that limits mobility and range. This paper presents a novel battery-free soft-robotics platform that can crawl through confined, enclosed, and hard-to-reach spaces (e. g. packages, machinery, pipes, etc.), hidden from view of heating infrastructure. This is achieved through the co-design of a soft robotics platform and integrated soft conductive traces that enable wireless (microwave) heating through remote stimulation. We achieve fast actuation through a careful choice of materials and the overall mechanical structure of the robot to maximize heating efficiency. Further, the robot is actively tracked through enclosed spaces using a mm Wave radar to direct heat to its location. We provide a detailed evaluation on the robot's heating efficiency, location-tracking accuracy and crawling speed.

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IROS Conference 2022 Conference Paper

Exploring mmWave Radar and Camera Fusion for High-Resolution and Long-Range Depth Imaging

Akarsh Prabhakara
Diana Zhang
Chao Li
Sirajum Munir
Aswin C. Sankaranarayanan
Anthony Rowe 0001
Swarun Kumar

Robotic geo-fencing and surveillance systems require accurate monitoring of objects if/when they violate perimeter restrictions. In this paper, we seek a solution for depth imaging of such objects of interest at high accuracy (few tens of cm) over extended ranges (up to 300 meters) from a single vantage point, such as a pole mounted platform. Unfortunately, the rich literature in depth imaging using camera, lidar and radar in isolation struggles to meet these tight requirements in real-world conditions. This paper proposes Metamoran, a solution that explores long-range depth imaging of objects of interest by fusing the strengths of two complementary technologies: mmWave radar and camera. Unlike cameras, mmWave radars offer excellent cm-scale depth resolution even at very long ranges. However, their angular resolution is at least 10x worse than camera systems. Fusing these two modalities is natural, but in scenes with high clutter and at long ranges, radar reflections are weak and experience spurious artifacts. Metamoran's core contribution is to leverage image segmentation and monocular depth estimation on camera images to help declutter radar and discover true object reflections. We perform a detailed evaluation of Metamoran's depth imaging capabilities in 400 diverse scenarios. Our evaluation shows that Metamoran estimates the depth of static objects up to 90 m away and moving objects up to 305 m away and with a median error of 28 cm, an improvement of 13 x over a naive radar+camera baseline and 23 x compared to monocular depth estimation.

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IROS Conference 2022 Conference Paper

Toolbox Release: A WiFi-Based Relative Bearing Framework for Robotics

Ninad Jadhav
Weiying Wang
Diana Zhang
Swarun Kumar
Stephanie Gil

This paper presents the WiFi-Sensor-for-Robotics (WSR) open-source toolbox 1 1 1 Code: https://github.com/Harvard-REACT/WSR-Toolbox Dataset: https://github.com/Harvard-REACT/WSR-Toolbox-Dataset Demo: https://github.com/Harvard-REACT/WSR-Toolbox/wiki/Demo.It enables robots in a team to obtain relative bearing to each other, even in nonline-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an open-source toolbox. It is motivated by the lack of easily deployable solutions that use robots' local resources (e. g WiFi) for sensing in NLOS. This has implications for multi-robot mapping and rendezvous, ad-hoc robot networks, and security in multi-robot teams, amongst other applications. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings and for a multi-robot localization use case. Empirical results for hardware experiments show that the bearing estimation from our toolbox achieves accuracy with mean and standard deviation of 1. 13 degrees, 11. 07 degrees in LOS and 6. 04 degrees, 26. 4 degrees for NLOS, respectively, in an indoor office environment.

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IJCAI Conference 2021 Conference Paper

Speech Recognition Using RFID Tattoos (Extended Abstract)

Jingxian Wang
Chengfeng Pan
Haojian Jin
Vaibhav Singh
Yash Jain
Jason I. Hong
Carmel Majidi
Swarun Kumar

This paper presents a radio-frequency (RF) based assistive technology for voice impairments (i. e. , dysphonia), which occurs in an estimated 1% of the global population. We specifically focus on acquired voice disorders where users continue to be able to make facial and lip gestures associated with speech. Despite the rich literature on assistive technologies in this space, there remains a gap for a solution that neither requires external infrastructure in the environment, battery-powered sensors on skin or body-worn manual input devices. We present RFTattoo, which to our knowledge is the first wireless speech recognition system for voice impairments using batteryless and flexible RFID tattoos. We design specialized wafer-thin tattoos attached around the user's face and easily hidden by makeup. We build models that process signal variations from these tattoos to a portable RFID reader to recognize various facial gestures corresponding to distinct classes of sounds. We then develop natural language processing models that infer meaningful words and sentences based on the observed series of gestures. A detailed user study with 10 users reveals 86% accuracy in reconstructing the top-100 words in the English language, even without the users making any sounds.

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