Author name cluster

Kapil D. Katyal

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.

12 papers

1 author row

ICRA Conference 2023 Conference Paper

Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances

Arun V. Reddy
Ketul Shah
William Paul
Rohita Mocharla
Judy Hoffman
Kapil D. Katyal
Dinesh Manocha
Celso M. de Melo

Human action recognition is a challenging problem, particularly when there is high variability in factors such as subject appearance, backgrounds and viewpoint. While deep neural networks (DNNs) have been shown to perform well on action recognition tasks, they typically require large amounts of high-quality labeled data to achieve robust performance across a variety of conditions. Synthetic data has shown promise as a way to avoid the substantial costs and potential ethical concerns associated with collecting and labeling enormous amounts of data in the real-world. However, synthetic data may differ from real data in important ways. This phenomenon, known as domain shift, can limit the utility of synthetic data in robotics applications. To mitigate the effects of domain shift, substantial effort is being dedicated to the development of domain adaptation (DA) techniques. Yet, much remains to be understood about how best to develop these techniques. In this paper, we introduce a new dataset called Robot Control Gestures (RoCoG-v2). The dataset is composed of both real and synthetic videos from seven gesture classes, and is intended to support the study of synthetic-to-real domain shift for video-based action recognition. Our work expands upon existing datasets by focusing the action classes on gestures for human-robot teaming, as well as by enabling investigation of domain shift in both ground and aerial views. We present baseline results using state-of-the-art action recognition and domain adaptation algorithms and offer initial insight on tackling the synthetic-to-real and ground-to-air domain shifts. Instructions on accessing the dataset can be found at https://github.com/reddyav1/RoCoG-v2.

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ICRA Conference 2022 Conference Paper

Complex Terrain Navigation via Model Error Prediction

Adam Polevoy
Craig Knuth
Katie M. Popek
Kapil D. Katyal

Robot navigation traditionally relies on building an explicit map that is used to plan collision-free trajectories to a desired target. In deformable, complex terrain, using geometric-based approaches can fail to find a path due to mischaracterizing deformable objects as rigid and impassable. Instead, we learn to predict an estimate of traversability of terrain regions and to prefer regions that are easier to navigate (e. g. , short grass over small shrubs). Rather than predicting collisions, we instead regress on realized error compared to a canonical system model. We train with an on-policy approach, resulting in successful navigation policies using as little as 50 minutes of training data split across simulation and real world. Our learning-based navigation system is a sample efficient short-term planner that we demonstrate on a Clearpath Husky navigating through a variety of terrain including grassland and forest.

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IROS Conference 2022 Conference Paper

Learning a Group-Aware Policy for Robot Navigation

Kapil D. Katyal
Yuxiang Gao
Jared Markowitz
Sara Pohland
Corban G. Rivera
I-Jeng Wang
Chien-Ming Huang 0001

Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mostly focused on modeling pedestrians as independent, intentional individuals, people move in groups; consequently, it is imperative for mobile robots to respect human groups when navigating around people. This paper explores learning group-aware navigation policies based on dynamic group formation using deep reinforcement learning. Through simulation experiments, we show that group-aware policies, compared to baseline policies that neglect human groups, achieve greater robot navigation performance (e. g. , fewer collisions), minimize violation of social norms and discomfort, and reduce the robot's movement impact on pedestrians. Our results contribute to the development of social navigation and the integration of mobile robots into human environments.

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ICRA Conference 2021 Conference Paper

High-Speed Robot Navigation using Predicted Occupancy Maps

Kapil D. Katyal
Adam Polevoy
Joseph L. Moore
Craig Knuth
Katie M. Popek

Safe and high-speed navigation is a key enabling capability for real world deployment of robotic systems. A significant limitation of existing approaches is the computational bottleneck associated with explicit mapping and the limited field of view (FOV) of existing sensor technologies. In this paper, we study algorithmic approaches that allow the robot to predict spaces extending beyond the sensor horizon for robust planning at high speeds. We accomplish this using a generative neural network trained from real-world data without requiring human annotated labels. Further, we extend our existing control algorithms to support leveraging the predicted spaces to improve collision-free planning and navigation at high speeds. Our experiments are conducted on a physical robot based on the MIT race car using an RGBD sensor where were able to demonstrate improved performance at 4 m/s compared to a controller not operating on predicted regions of the map.

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ICRA Conference 2021 Conference Paper

Out-of-Distribution Robustness with Deep Recursive Filters

Kapil D. Katyal
I-Jeng Wang
Gregory D. Hager

Accurate state and uncertainty estimation is imperative for mobile robots and self driving vehicles to achieve safe navigation in pedestrian rich environments. A critical component of state and uncertainty estimation for robot navigation is to perform robustly under out-of-distribution noise. Traditional methods of state estimation decouple perception and state estimation making it difficult to operate on noisy, high dimensional data. Here, we describe an approach that combines the expressiveness of deep neural networks with principled approaches to uncertainty estimation found in recursive filters. We particularly focus on techniques that provide better robustness to out-of-distribution noise and demonstrate applicability of our approach on two scenarios: a simple noisy pendulum state estimation problem and real world pedestrian localization using the nuScenes dataset [1]. We show that our approach improves state and uncertainty estimation compared to baselines while achieving approximately 3× improvement in computational efficiency.

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IROS Conference 2021 Conference Paper

Robust Policy Search for an Agile Ground Vehicle Under Perception Uncertainty

Shahriar Sefati
Subhransu Mishra
Matthew Sheckells
Kapil D. Katyal
Jin Bai 0001
Gregory D. Hager
Marin Kobilarov

Learning robust policies for robotic systems operating in presence of uncertainty is a challenging task. For safe navigation, in addition to the natural stochasticity of the environment and vehicle dynamics, the perception uncertainty associated with dynamic entities, e. g. pedestrians, must be accounted for during motion planning. To this end, we construct an algorithm with built-in robustness to uncertainty by directly minimizing an upper confidence bound on the expected cost of trajectories instead of employing a standard approach based on minimizing the expected cost itself. Perception uncertainty is incorporated into the policy search framework by predicting each pedestrian’s intent belief and propagating their state distribution in time using closed-loop goal-directed dynamics. We train the policy in simulation and show that it could be transferred to an agile ground vehicle for successful autonomous robot navigation in presence of pedestrians with perception uncertainty. We further show the superior performance of this policy over a policy that does not consider pedestrian intent and perception uncertainty.

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ICRA Conference 2020 Conference Paper

Intent-Aware Pedestrian Prediction for Adaptive Crowd Navigation

Kapil D. Katyal
Gregory D. Hager
Chien-Ming Huang 0001

Mobile robots capable of navigating seamlessly and safely in pedestrian rich environments promise to bring robotic assistance closer to our daily lives. In this paper we draw on insights of how humans move in crowded spaces to explore how to recognize pedestrian navigation intent, how to predict pedestrian motion and how a robot may adapt its navigation policy dynamically when facing unexpected human movements. Our approach is to develop algorithms that replicate this behavior. We experimentally demonstrate the effectiveness of our prediction algorithm using real-world pedestrian datasets and achieve comparable or better prediction accuracy compared to several state-of-the-art approaches. Moreover, we show that confidence of pedestrian prediction can be used to adjust the risk of a navigation policy adaptively to afford the most comfortable level as measured by the frequency of personal space violation in comparison with baselines. Furthermore, our adaptive navigation policy is able to reduce the number of collisions by 43% in the presence of novel pedestrian motion not seen during training.

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ICRA Conference 2019 Conference Paper

Uncertainty-Aware Occupancy Map Prediction Using Generative Networks for Robot Navigation

Kapil D. Katyal
Katie M. Popek
Chris Paxton 0001
Philippe Burlina
Gregory D. Hager

Efficient exploration through unknown environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future motion is often severely limited by sensor field of view (FOV). By contrast, biological systems routinely make decisions by taking into consideration what might exist beyond their FOV based on prior experience. We present an approach for predicting occupancy map representations of sensor data for future robot motions using deep neural networks. We develop a custom loss function used to make accurate prediction while emphasizing physical boundaries. We further study extensions to our neural network architecture to account for uncertainty and ambiguity inherent in mapping and exploration. Finally, we demonstrate a combined map prediction and information-theoretic exploration strategy using the variance of the generated hypotheses as the heuristic for efficient exploration of unknown environments.

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ICRA Conference 2019 Conference Paper

Visual Robot Task Planning

Chris Paxton 0001
Yotam Barnoy
Kapil D. Katyal
Raman Arora
Gregory D. Hager

Prospection is key to solving challenging problems in new environments, but it has not been deeply explored as applied to task planning for perception-driven robotics. We propose visual robot task planning, where we take in an input image and must generate a sequence of high-level actions and associated observations that achieve some task. In this paper, we describe a neural network architecture and associated planning algorithm that (1) learns a representation of the world that can generate prospective futures, (2) uses this generative model to simulate the result of sequences of high-level actions in a variety of environments, and (3) evaluates these actions via a variant of Monte Carlo Tree Search to find a viable solution to a particular problem. Our approach allows us to visualize intermediate motion goals and learn to plan complex activity from visual information, and used this to generate and visualize task plans on held-out examples of a block-stacking simulation.

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IROS Conference 2018 Conference Paper

Autonomous Grasping Robotic Aerial System for Perching (AGRASP)

Katie M. Popek
Matthew S. Johannes
Kevin C. Wolfe
Rachel A. Hegeman
Jessica M. Hatch
Joseph L. Moore
Kapil D. Katyal
Bryanna Y. Yeh

This paper presents an autonomous perching concept for multirotor aerial vehicles. The Autonomous Grasping Robotic Aerial System for Perching (AGRASP)represents a novel integration of robotics perception, vision-based path planning, and biomimetically-inspired manipulation on a small, lightweight aerial robot with highly-constrained sensor and processing capacity. Computationally lightweight perception algorithms pull candidate perch structures out of a complex environment with no a priori knowledge of the operational space. The innovative manipulator design combines both active grasp and passive grip enabling it to maintain hold on the perch even with all power off. We experimentally demonstrate, for the first time, a quadrotor autonomously detecting and landing on a perch relying solely on onboard sensing and processing.

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IROS Conference 2014 Conference Paper

Approaches to robotic teleoperation in a disaster scenario: From supervised autonomy to direct control

Kapil D. Katyal
Christopher Y. Brown
Steven A. Hechtman
Matthew P. Para
Timothy G. McGee
Kevin C. Wolfe
Ryan J. Murphy
Michael D. M. Kutzer

The ability of robotic systems to effectively address disaster scenarios that are potentially dangerous for human operators is continuing to grow as a research and development field. This leverages research from areas such as bimanual manipulation, dexterous grasping, bipedal locomotion, computer vision, sensing, object segmentation, varying degrees of autonomy, and operator control/feedback. This paper describes the development of a semi-autonomous bimanual dexterous robotic system that comes to the aid of a mannequin simulating an injured victim by operating a fire extinguisher, affixing a cervical collar, cooperatively placing the victim on a spineboard with another bimanual robot, and relocating the victim. This system accomplishes these tasks through a series of control modalities that range from supervised autonomy to full teleoperation and allows the control model to be chosen and optimized for a specific subtask. We present a description of the hardware platform, the software control architecture, a human-in-the-loop computer vision algorithm, and an infrastructure to use a variety of user input devices in combination with autonomous control to compete several dexterous tasks. The effectiveness of the system was demonstrated in both laboratory and live outdoor demonstrations.

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IROS Conference 2013 Conference Paper

Experimental validation of imposed safety regions for neural controlled human patient self-feeding using the modular prosthetic limb

Brock A. Wester
Matthew P. Para
Ashok Sivakumar
Michael D. M. Kutzer
Kapil D. Katyal
Alan D. Ravitz
James D. Beaty
Michael P. McLoughlin

This paper presents the experimental validation of software-based safety features implemented during the control of a prosthetic limb in self-feeding tasks with a human patient. To ensure safe operation during patient controlled movements of the limb, velocity-based virtual fixtures are constructed with respect to the patient's location and orientation relative to the limb. These imposed virtual fixtures or safety zones modulate the allowable movement direction and speed of the limb to ensure patient safety during commanded limb trajectories directed toward the patient's body or environmental obstacles. In this implementation, the Modular Prosthetic Limb (MPL) will be controlled by a quadriplegic patient using implanted intracortical electrodes. These virtual fixtures leverage existing sensors internal to the MPL and operate in conjunction with the existing limb control. Validation of the virtual fixtures was conducted by executing a recorded set of limb control inputs while collecting both direct feedback from the limb sensors and ground truth measurements of the limb configuration using a Vicon tracking system. Analysis of the collected data indicates that the system performed within the limitations prescribed by the imposed virtual fixtures. This successful implementation and validation enabled the approved clinical use of the MPL system for a neural controlled self-feeding task.

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