arXiv subjects

Joydeep Biswas

Publications and source records attributed to Joydeep Biswas.

98 records · Page 6Linked to original sources

arXiv2020

SMT-based Robot Transition Repair

State machines are a common model for robot behaviors. Transition functions often rely on parameterized conditions to model preconditions for the controllers, where the correct values of the parameters depend on factors relating to the environment or the specific robot. In the absence of specific calibration procedures a roboticist must painstakingly adjust the parameters through a series of trial and error experiments. In this process, identifying when the robot has taken an incorrect action, and what should be done is straightforward, but finding the right parameter values can be difficult. We present an alternative approach that we call, interactive SMT-based Robot Transition Repair. During execution we record an execution trace of the transition function, and we ask the roboticist to identify a few instances where the robot has transitioned incorrectly, and what the correct transition should have been. A user supplies these corrections based on the type of error to repair, and an automated analysis of the traces partially evaluates the transition function for each correction. This system of constraints is then formulated as a MaxSMT problem, where the solution is a minimal adjustment to the parameters that satisfies the maximum number of constraints. In order to identify a repair that accurately captures user intentions and generalizes to novel scenarios, solutions are explored by iteratively adding constraints to the MaxSMT problem to yield sets of alternative repairs. We test with state machines from multiple domains including robot soccer and autonomous driving, and we evaluate solver based repair with respect to solver choice and optimization hyperparameters. Our results demonstrate that SRTR can repair a variety of states machines and error types 1) quickly, 2) with small numbers of corrections, while 3) not overcorrecting state machines and harming generalized performance.

cs.RO↗

arXiv2019

Making High-Performance Robots Safe and Easy to Use for an Introduction to Computing

Robots are a popular platform for introducing computing and artificial intelligence to novice programmers. However, programming state-of-the-art robots is very challenging, and requires knowledge of concurrency, operation safety, and software engineering skills, which can take years to teach. In this paper, we present an approach to introducing computing that allows students to safely and easily program high-performance robots. We develop a platform for students to program RoboCup Small Size League robots using JavaScript. The platform 1) ensures physical safety at several levels of abstraction, 2) allows students to program robots using the JavaScript in the browser, without the need to install software, and 3) presents a simplified JavaScript semantics that shields students from confusing language features. We discuss our experience running a week-long workshop using this platform, and analyze over 3,000 student-written program revisions to provide empirical evidence that our approach does help students.

cs.CY↗

arXiv2019

Automatic Failure Recovery for End-User Programs on Service Mobile Robots

For service mobile robots to be most effective, it must be possible for non-experts and even end-users to program them to do new tasks. Regardless of the programming method (e.g., by demonstration or traditional programming), robot task programs are challenging to write, because they rely on multiple actions to succeed, including human-robot interactions. Unfortunately, interactions are prone to fail, because a human may perform the wrong action (e.g., if the robot's request is not clear). Moreover, when the robot cannot directly observe the human action, it may not detect the failure until several steps after it occurs. Therefore, writing fault-tolerant robot tasks is beyond the ability of non-experts. This paper presents a principled approach to detect and recover from a broad class of failures that occur in end-user programs on service mobile robots. We present a two-tiered Robot Task Programming Language (RTPL): 1) an expert roboticist uses a specification language to write a probabilistic model of the robot's actions and interactions, and 2) a non-expert then writes an ordinary sequential program for a particular task. The RTPL runtime system executes the task program sequentially, while using the probabilistic model to build a Bayesian network that tracks possible, unobserved failures. If an error is observed, RTPL uses Bayesian inference to find the likely root cause of the error, and then attempts to re-execute a portion of the program for recovery. Our empirical results show that RTPL 1) allows complex tasks to be written concisely, 2) correctly identifies the root cause of failure, and 3) allows multiple tasks to recover from a variety of errors, without task-specific error-recovery code.

cs.RO↗

arXiv2019

IVOA: Introspective Vision for Obstacle Avoidance

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.

cs.RO↗

arXiv2018

A Real-Time Solver For Time-Optimal Control Of Omnidirectional Robots with Bounded Acceleration

We are interested in the problem of time-optimal control of omnidirectional robots with bounded acceleration (TOC-ORBA). While there exist approximate solutions for such robots, and exact solutions with unbounded acceleration, exact solvers to the TOC-ORBA problem have remained elusive until now. In this paper, we present a real-time solver for true time-optimal control of omnidirectional robots with bounded acceleration. We first derive the general parameterized form of the solution to the TOC-ORBA problem by application of Pontryagin's maximum principle. We then frame the boundary value problem of TOC-ORBA as an optimization problem over the parametrized control space. To overcome local minima and poor initial guesses to the optimization problem, we introduce a two-stage optimal control solver (TSOCS): The first stage computes an upper bound to the total time for the TOC-ORBA problem and holds the time constant while optimizing the parameters of the trajectory to approach the boundary value conditions. The second stage uses the parameters found by the first stage, and relaxes the constraint on the total time to solve for the parameters of the complete TOC-ORBA problem. We further implement TSOCS as a closed loop controller to overcome actuation errors on real robots in real-time. We empirically demonstrate the effectiveness of TSOCS in simulation and on real robots, showing that 1) it runs in real time, generating solutions in less than 0.5ms on average; 2) it generates faster trajectories compared to an approximate solver; and 3) it is able to solve TOC-ORBA problems with non-zero final velocities that were previously unsolvable in real-time.

cs.RO↗

arXiv2018

Interactive Robot Transition Repair With SMT

Complex robot behaviors are often structured as state machines, where states encapsulate actions and a transition function switches between states. Since transitions depend on physical parameters, when the environment changes, a roboticist has to painstakingly readjust the parameters to work in the new environment. We present interactive SMT-based Robot Transition Repair (SRTR): instead of manually adjusting parameters, we ask the roboticist to identify a few instances where the robot is in a wrong state and what the right state should be. A lightweight automated analysis of the transition function's source code then 1) identifies adjustable parameters, 2) converts the transition function into a system of logical constraints, and 3) formulates the constraints and user-supplied corrections as MaxSMT problem that yields new parameter values. Our evaluation shows that SRTR is effective on real robots and in simulation. We show that SRTR finds new parameters 1) quickly, 2) with only a few corrections, and 3) that the parameters generalize to new scenarios. We also show that a simple state machine corrected by SRTR can out-perform a more complex, expert-tuned state machine in the real world.

cs.RO↗

arXiv2018

Localization under Topological Uncertainty for Lane Identification of Autonomous Vehicles

Autonomous vehicles (AVs) require accurate metric and topological location estimates for safe, effective navigation and decision-making. Although many high-definition (HD) roadmaps exist, they are not always accurate since public roads are dynamic, shaped unpredictably by both human activity and nature. Thus, AVs must be able to handle situations in which the topology specified by the map does not agree with reality. We present the Variable Structure Multiple Hidden Markov Model (VSM-HMM) as a framework for localizing in the presence of topological uncertainty, and demonstrate its effectiveness on an AV where lane membership is modeled as a topological localization process. VSM-HMMs use a dynamic set of HMMs to simultaneously reason about location within a set of most likely current topologies and therefore may also be applied to topological structure estimation as well as AV lane estimation. In addition, we present an extension to the Earth Mover's Distance which allows uncertainty to be taken into account when computing the distance between belief distributions on simplices of arbitrary relative sizes.

cs.RO↗

arXiv2017

Human-in-the-Loop SLAM

Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users. For such scenarios, where state-of-the-art mapping algorithms produce globally inconsistent maps, we introduce a systematic approach to incorporating sparse human corrections, which we term Human-in-the-Loop Simultaneous Localization and Mapping (HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM accepts approximate, potentially erroneous, and rank-deficient human input, infers the intended correction via expectation maximization (EM), back-propagates the extracted corrections over the pose graph, and finally jointly optimizes the factor graph including the human inputs as human correction factor terms, to yield globally consistent large-scale maps. We thus contribute an EM formulation for inferring potentially rank-deficient human corrections to mapping, and human correction factor extensions to the factor graphs for pose graph SLAM that result in a principled approach to joint optimization of the pose graph while simultaneously accounting for multiple forms of human correction. We present empirical results showing the effectiveness of HitL-SLAM at generating globally accurate and consistent maps even when given poor initial estimates of the map.

cs.HC↗