Rahul Peddi

I am a Ph.D. student in Systems Engineering at the University of Virginia. I work in Autonomous Mobile Robots Lab and Link Lab under Prof. Nicola Bezzo. Before joining UVA, I received a BS degree in Mechanical and Nuclear Engineering at Virginia Commonwealth University.

My research focuses on human-robot interaction, social navigation, proactive motion planning under uncertainty, and multi-agent/actor planning and control.

Research

Parameter-free Regression-based Autonomous Control of Off-the-shelf Quadrotor UAVs

Autonomous flight in unmanned aerial vehicles (UAVs) generally requires platform-specific knowledge of the dynamical parameters and control architecture. Recently, UAVs have become more accessible with off-the-shelf options that are well-tuned and stable for user teleoperation but due to unknown model parameters, they are typically not ready for autonomous operations.

We develop a method to enable autonomous flight on vehicles that are designed for teleoperation with minimal knowledge of the dynamical and controller parameters. The proposed method uses a basic knowledge of the control and dynamic architecture along with human teleoperated trajectories for demonstration learning. We use a regression-based model with statistical validation to minimize errors in input generation.

[ICUAS 2019]

pf_motiv.gif
peddi3-0226-small.gif

A Data-driven Framework for Proactive Intention-Aware Motion Planning of a Robot in a Human Environment

For safe and efficient human-robot interaction, a robot needs to predict and understand the intentions of humans who share the same space. Mobile robots are traditionally built to be reactive, moving in unnatural ways without following social protocol, hence forcing people to behave very differently from human-human interaction rules, which can be overcome if robot were proactive.

We build an intention-aware proactive motion planning strategy for mobile robots that coexist with multiple humans. We propose a framework that uses Hidden Markov Model (HMM) theory to: i) predict future states and estimate the likelihood that humans will cross the path of a robot, and ii) concurrently learn, update, and improve the predictive model with new observations at run-time. We also use stochastic reachability analysis to identify multiple possibilities of future states and a control scheme that leverages temporal virtual physics inspired by spring-mass system to enable proactive motion planning.

[IROS 2020]

pictorial2.jpg
reachv5b.png
motionplan6.png