Author name cluster

Jason M. Gregory

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers

1 author row

ICRA Conference 2023 Conference Paper

Causal Inference for De-biasing Motion Estimation from Robotic Observational Data

Junhong Xu
Kai Yin
Jason M. Gregory
Lantao Liu

Robot data collected in complex real-world scenarios are often biased due to safety concerns, human preferences, and mission or platform constraints. Consequently, robot learning from such observational data poses great challenges for accurate parameter estimation. We propose a principled causal inference framework for robots to learn the parameters of a stochastic motion model using observational data. Specifically, we leverage the de-biasing functionality of the potential-outcome causal inference framework, the Inverse Propensity Weighting (IPW), and the Doubly Robust (DR) methods, to obtain a better parameter estimation of the robot's stochastic motion model. The IPW is a re-weighting approach to ensure unbiased estimation, and the DR approach further combines any two estimators to strengthen the unbiased result even if one of these estimators is biased. We then develop an approximate policy iteration algorithm using the bias-eliminated estimated state transition function. We validate our framework using both simulation and real-world experiments, and the results have revealed that the proposed causal inference-based navigation and control framework can correctly and efficiently learn the parameters from biased observational data.

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ICRA Conference 2023 Conference Paper

How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability

Mateo Guaman Castro
Samuel Triest
Wenshan Wang
Jason M. Gregory
Felix A. Sanchez
John G. Rogers
Sebastian A. Scherer

Estimating terrain traversability in off-road environments requires reasoning about complex interaction dynamics between the robot and these terrains. However, it is challenging to create informative labels to learn a model in a supervised manner for these interactions. We propose a method that learns to predict traversability costmaps by combining exteroceptive environmental information with proprioceptive terrain interaction feedback in a self-supervised manner. Additionally, we propose a novel way of incorporating robot velocity into the costmap prediction pipeline. We validate our method in multiple short and large-scale navigation tasks on challenging off-road terrains using two different large, all-terrain robots. Our short-scale navigation results show that using our learned costmaps leads to overall smoother navigation, and provides the robot with a more fine-grained understanding of the robot-terrain interactions. Our large-scale navigation trials show that we can reduce the number of interventions by up to 57% compared to an occupancy-based navigation baseline in challenging off-road courses ranging from 400 m to 3150 m. Appendix and full experiment videos can be found in our website: https://mateoguaman.github.io/hdif.

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IROS Conference 2023 Conference Paper

IDA: Informed Domain Adaptive Semantic Segmentation

Zheng Chen 0016
Zhengming Ding
Jason M. Gregory
Lantao Liu

Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDASS), which aims to transfer knowledge from a well-annotated (source) domain to an unlabeled (target) domain. Existing self-training methods usually adopt the popular region-based mixup techniques with a random sampling strategy, which unfortunately ignores the dynamic evolution of different semantics across various domains as training proceeds. To improve the UDA-SS performance, we propose an Informed Domain Adaptation (IDA) model, a self-training framework that mixes the data based on class-level segmentation performance, which aims to emphasize small-region semantics during mixup. In our IDA model, the class-level performance is tracked by an expected confidence score (ECS). We then use a dynamic schedule to determine the mixing ratio for data in different domains. Extensive experimental results reveal that our proposed method is able to outperform the state-of-the-art UDA-SS method by a margin of 1. 1 mIoU in the adaptation of GTA-V to Cityscapes and of 0. 9 mIoU in the adaptation of SYNTHIA to Cityscapes. Code link: https://github.com/ArlenCHEN/IDA.git

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IROS Conference 2023 Conference Paper

Terrain-Aware Kinodynamic Planning with Efficiently Adaptive State Lattices for Mobile Robot Navigation in Off-Road Environments

Eric R. Damm
Jason M. Gregory
Eli S. Lancaster
Felix A. Sanchez
Daniel M. Sahu
Thomas M. Howard

To safely traverse non-flat terrain, robots must account for the influence of terrain shape in their planned motions. Terrain-aware motion planners use an estimate of the vehicle roll and pitch as a function of pose, vehicle suspension, and ground elevation map to weigh the cost of edges in the search space. Encoding such information in a traditional two-dimensional cost map is limiting because it is unable to capture the influence of orientation on the roll and pitch estimates from sloped terrain. The research presented herein addresses this problem by encoding kinodynamic information in the edges of a recombinant motion planning search space based on the Efficiently Adaptive State Lattice (EASL). This approach, which we describe as a Kinodynamic Efficiently Adaptive State Lattice (KEASL), differs from the prior representation in two ways. First, this method uses a novel encoding of velocity and acceleration constraints and vehicle direction at expanded nodes in the motion planning graph. Second, this approach describes additional steps for evaluating the roll, pitch, constraints, and velocities associated with poses along each edge during search in a manner that still enables the graph to remain recombinant. Velocities are computed using an iterative bidirectional method using Eulerian integration that more accurately estimates the duration of edges that are subject to terrain-dependent velocity limits. Real-world experiments on a Clearpath Robotics Warthog Unmanned Ground Vehicle were performed in a non-flat, unstructured environment. Results from 2093 planning queries from these experiments showed that KEASL provided a more efficient route than EASL in 83. 72% of cases when EASL plans were adjusted to satisfy terrain-dependent velocity constraints. An analysis of relative runtimes and differences between planned routes is additionally presented. These results reinforce the importance of considering kinodynamic constraints for motion planning in non-flat environments and illustrate how such information can be encoded in an adaptive recombinant motion planning search space.

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ICRA Conference 2022 Conference Paper

Active Learning for Testing and Evaluation in Field Robotics: A Case Study in Autonomous, Off-Road Navigation

Jason M. Gregory
Daniel M. Sahu
Eli S. Lancaster
Felix A. Sanchez
Trevor Rocks
Brian Kaukeinen
Jonathan Fink
Satyandra K. Gupta

Testing and evaluation of field robotic systems requires both experimentation in representative conditions and human supervision to effectively assess components, manage risk, and interpret results. Due to the complexity of robotic sys-tems, we argue this experimentation should be done adaptively by using insights gained from previous trials. Furthermore, we envision an advisory system that could assist experimenters with selecting trial configurations by learning and accounting for human preferences and risk tolerances; however, formal methods for human decision making in the context of field robotic experimentation remains an open question. In this work, we present and analyze a case study for how decisions were made during the testing and evaluation of an off-road, autonomous navigation system. From the perspective of active learning, we find that Bayesian Optimization is a promising mathematical framework for modeling human decision making in adaptive experimental design of field robotics and that a combination of the EI, KG, and PES acquisition functions would likely be useful for realizing an advisory system.

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IROS Conference 2020 Conference Paper

Generating Alerts to Assist With Task Assignments in Human-Supervised Multi-Robot Teams Operating in Challenging Environments

Sarah Al-Hussaini
Jason M. Gregory
Yuxiang Guan
Satyandra K. Gupta

In a mission with considerable uncertainty due to intermittent communications, degraded information flow, and failures, humans need to assess both the current and expected future states, and update task assignments to robots as quickly as possible. We present a forward simulation-based alert system that proactively notifies the human supervisor of possible, negatively-impactful events, which provides an opportunity for the human to retask agents to avoid undesirable scenarios. We propose methods for speeding up mission simulations and extracting alerts from simulation data in order to enable real-time alert generation suitable for time-critical missions. We present the results from a user trial and verify our hypothesis that the decision making performance of human supervisors can be improved by introducing forward simulation-based alerts.

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ICRA Conference 2020 Conference Paper

Test Your SLAM! The SubT-Tunnel dataset and metric for mapping

John G. Rogers
Jason M. Gregory
Jonathan Fink
Ethan Stump

This paper presents an approach and introduces new open-source tools that can be used to evaluate robotic mapping algorithms. Also described is an extensive subterranean mine rescue dataset based upon the DARPA Subterranean (SubT) challenge including professionally surveyed ground truth. Finally, some commonly available approaches are evaluated using this metric.

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IROS Conference 2018 Conference Paper

Generation of Context-Dependent Policies for Robot Rescue Decision-Making in Multi-Robot Teams

Sarah Al-Hussaini
Jason M. Gregory
Satyandra K. Gupta

We propose a scalable, parallelizable policy synthesis framework intended for a robot presented with the decision of exploration or rescue, given some time-varying, stochastic mission conditions, referred to as context. We demonstrate the feasibility of such a solution using physics-based simulations to synthesize a policy in a computationally-efficient manner and exhibit superior performance with regards to the minimization of probability of mission failure when compared to two feasible baseline approaches. Furthermore, we present preliminary results that suggest our approach is robust to errors in the state estimation used to build mission context, which further supports the notion of real-world applicability.

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IROS Conference 2016 Conference Paper

Towards online characterization of autonomously navigating robots in unstructured environments

Jeffrey N. Twigg
Jason M. Gregory
Jonathan Fink

Autonomous platforms are confronted by a diversity of challenges in unstructured environments, which make monitoring performance a non-trivial task. Some of these environments are so complex that they preclude persistent, nearby operator oversight. This absence of oversight motivates the need for an online monitoring system, specifically for robots operating in difficult environments. We develop a test methodology and set of online monitoring metrics by extending methods for characterizing robotic-systems using offline metrics. We implement this test methodology in an unstructured, outdoor environment and show the resulting performance information gained from our online monitoring solution. This online monitoring approach is generalizable such that it characterizes any robotic system that meets our set of hardware and software criteria.

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