Author name cluster

Sadegh Rabiee

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7 papers

2 author rows

AIJ Journal 2023 Journal Article

Introspective perception for mobile robots

Sadegh Rabiee
Joydeep Biswas

Perception algorithms that provide estimates of their uncertainty are crucial to the development of autonomous robots that can operate in challenging and uncontrolled environments. Such perception algorithms provide the means for having risk-aware robots that reason about the probability of successfully completing a task when planning. There exist perception algorithms that come with models of their uncertainty; however, these models are often developed with assumptions, such as perfect data associations, that do not hold in the real world. Hence the resultant estimated uncertainty is a weak lower bound. To tackle this problem we present introspective perception — a novel approach for predicting accurate estimates of the uncertainty of perception algorithms deployed on mobile robots. By exploiting sensing redundancy and consistency constraints naturally present in the data collected by a mobile robot, introspective perception learns an empirical model of the error distribution of perception algorithms in the deployment environment and in an autonomously supervised manner. In this paper, we present the general theory of introspective perception and demonstrate successful implementations for two different perception tasks. We provide empirical results on challenging real-robot data for introspective stereo depth estimation and introspective visual simultaneous localization and mapping and show that they learn to predict their uncertainty with high accuracy and leverage this information to significantly reduce state estimation errors for an autonomous mobile robot.

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IROS Conference 2022 Conference Paper

High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization

Pranav Atreya
Haresh Karnan
Kavan Singh Sikand
Xuesu Xiao
Sadegh Rabiee
Joydeep Biswas

Accurate control of robots at high speeds requires a control system that can take into account the kinodynamic interactions of the robot with the environment. Prior works on learning inverse kinodynamic (IKD) models of robots have shown success in capturing the complex kinodynamic effects. However, the types of control problems these approaches can be applied to are limited only to that of following pre-computed kinodynamically feasible trajectories. In this paper we present Optim-FKD, a new formulation for accurate, high-speed robot control that makes use of a learned forward kinodynamic (FKD) model and non-linear least squares optimization. Optim-FKD can be used for accurate, high speed control on any control task specifiable by a non-linear least squares objective. Optim-FKD can solve for control objectives such as path following and time-optimal control in real time, without needing access to pre-computed kinodynamically feasible trajectories. We empirically demonstrate these abilities of our approach through experiments on a scale one-tenth autonomous car. Our results show that Optim-FKD can follow desired trajectories more accurately and can find better solutions to optimal control problems than baseline approaches.

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IROS Conference 2022 Conference Paper

VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics

Haresh Karnan
Kavan Singh Sikand
Pranav Atreya
Sadegh Rabiee
Xuesu Xiao
Garrett Warnell
Peter Stone 0001
Joydeep Biswas

One of the key challenges in high-speed off-road navigation on ground vehicles is that the kinodynamics of the vehicle-terrain interaction can differ dramatically depending on the terrain. Previous approaches to addressing this challenge have considered learning an inverse kinodynamics (IKD) model, conditioned on inertial information of the vehicle to sense the kinodynamic interactions. In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic interactions of the vehicle with the terrain in the future. To this end, we introduce Visual-Inertial Inverse Kinodynamics (VI-IKD), a novel learning based IKD model that is conditioned on visual information from a terrain patch ahead of the robot in addition to past inertial information, enabling it to anticipate kinodynamic interactions in the future. We validate the effectiveness of VI-IKD in accurate high-speed off-road navigation experimentally on a scale 1/5 UT-AlphaTruck off-road autonomous vehicle in both indoor and outdoor environments and show that compared to other state-of-the-art approaches, VI-IKD enables more accurate and robust off-road navigation on a variety of different terrains at speeds of up to 3. 5m/s.

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ICRA Conference 2022 Conference Paper

Visual Representation Learning for Preference-Aware Path Planning

Kavan Singh Sikand
Sadegh Rabiee
Adam Uccello
Xuesu Xiao
Garrett Warnell
Joydeep Biswas

Autonomous mobile robots deployed in outdoor environments must reason about different types of terrain for both safety (e. g. , prefer dirt over mud) and deployer preferences (e. g. , prefer dirt path over flower beds). Most existing solutions to this preference-aware path planning problem use semantic segmentation to classify terrain types from camera images, and then ascribe costs to each type. Unfortunately, there are three key limitations of such approaches - they 1) require preenumeration of the discrete terrain types, 2) are unable to handle hybrid terrain types (e. g. , grassy dirt), and 3) require expensive labelled data to train visual semantic segmentation. We introduce Visual Representation Learning for Preference-Aware Path Planning (VRL-PAP), an alternative approach that overcomes all three limitations: VRL-PAP leverages un-labelled human demonstrations of navigation to autonomously generate triplets for learning visual representations of terrain that are viewpoint invariant and encode terrain types in a continuous representation space. The learned representations are then used along with the same unlabelled human navigation demonstrations to learn a mapping from the representation space to terrain costs. At run time, VRL-PAP maps from images to representations and then representations to costs to perform preference-aware path planning. We present empirical results from challenging outdoor settings that demonstrate VRL-PAP 1) is successfully able to pick paths that reflect demonstrated preferences, 2) is comparable in execution to geometric navigation with a highly detailed manually annotated map (without requiring such annotations), 3) is able to generalize to novel terrain types with minimal additional unlabeled demonstrations.

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IROS Conference 2021 Conference Paper

Robofleet: Open Source Communication and Management for Fleets of Autonomous Robots

Kavan Singh Sikand
Logan Zartman
Sadegh Rabiee
Joydeep Biswas

Long-term deployment of a fleet of mobile robots requires reliable and secure two-way communication channels between individual robots and remote human operators for supervision and tasking. Existing open-source solutions to this problem degrade in performance in challenging real-world situations such as intermittent and low-bandwidth connectivity, do not provide security control options, and can be computationally expensive on hardware-constrained mobile robot platforms. In this paper, we present Robofleet, a lightweight open-source system which provides inter-robot communication, remote monitoring, and remote tasking for a heterogenous fleet of ROS-enabled service-mobile robots that is designed with the practical goals of resilience to network variance and security control in mind. Robofleet supports multi-user, multi-robot communication via a central server. This architecture deduplicates network traffic between robots, significantly reducing overall network load when compared with native ROS communication. This server also functions as a single entrypoint into the system, enabling security control and user authentication. Individual robots run the lightweight Robofleet client, which is responsible for exchanging messages with the Robofleet server. It automatically adapts to adverse network conditions through backpressure monitoring as well as topic-level priority control, ensuring that safety-critical messages are successfully transmitted. Finally, the system includes a web-based visualization tool that can be run on any internet-connected, browser-enabled device to monitor and control the fleet. We compare Robofleet to existing methods of robotic communication, and demonstrate that it provides superior resilience to network variance while maintaining performance that exceeds that of widely-used systems.

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ICRA Conference 2019 Conference Paper

A Friction-Based Kinematic Model for Skid-Steer Wheeled Mobile Robots

Sadegh Rabiee
Joydeep Biswas

Skid-steer drive systems are widely used in mobile robot platforms. Such systems are subject to significant slippage and skidding during normal operation due to their nature. The ability to predict and compensate for such slippages in the forward kinematics of these types of robots is of great importance and provides the means for accurate control and safe navigation. In this work, we propose a new kinematic model capable of slip prediction for skid-steer wheeled mobile robots (SSWMRs). The proposed model outperforms the state-of-the-art in terms of both translational and rotational prediction error on a dataset composed of more than 6 km worth of trajectories traversed by a skid-steer robot. We also publicly release our dataset to serve as a benchmark for system identification and model learning research for SSWMRs.

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IROS Conference 2019 Conference Paper

IVOA: Introspective Vision for Obstacle Avoidance

Sadegh Rabiee
Joydeep Biswas

Vision, as an inexpensive yet information rich sensor, is commonly used for perception on autonomous mobile robots. Unfortunately, accurate vision-based perception requires a number of assumptions about the environment to hold – some examples of such assumptions, depending on the perception algorithm at hand, include purely lambertian surfaces, texture-rich scenes, absence of aliasing features, and refractive surfaces. In this paper, we present an approach for introspective vision for obstacle avoidance (IVOA) – by leveraging a supervisory sensor that is occasionally available, we detect failures of stereo vision-based perception from divergence in plans generated by vision and the supervisory sensor. By projecting the 3D coordinates where the plans agree and disagree onto the images used for vision-based perception, IVOA generates a training set of reliable and unreliable image patches for perception. We then use this training dataset to learn a model of which image patches are likely to cause failures of the vision-based perception algorithm. Using this model, IVOA is then able to predict whether the relevant image patches in the observed images are likely to cause failures due to vision (both false positives and false negatives). We empirically demonstrate with extensive real-world data from both indoor and outdoor environments, the ability of IVOA to accurately predict the failures of two distinct vision algorithms.

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