arXiv subjects

Taskin Padir

Publications and source records attributed to Taskin Padir.

At least 37 records · Page 2Linked to original sources

arXiv2024

StereoNavNet: Learning to Navigate using Stereo Cameras with Auxiliary Occupancy Voxels

Visual navigation has received significant attention recently. Most of the prior works focus on predicting navigation actions based on semantic features extracted from visual encoders. However, these approaches often rely on large datasets and exhibit limited generalizability. In contrast, our approach draws inspiration from traditional navigation planners that operate on geometric representations, such as occupancy maps. We propose StereoNavNet (SNN), a novel visual navigation approach employing a modular learning framework comprising perception and policy modules. Within the perception module, we estimate an auxiliary 3D voxel occupancy grid from stereo RGB images and extract geometric features from it. These features, along with user-defined goals, are utilized by the policy module to predict navigation actions. Through extensive empirical evaluation, we demonstrate that SNN outperforms baseline approaches in terms of success rates, success weighted by path length, and navigation error. Furthermore, SNN exhibits better generalizability, characterized by maintaining leading performance when navigating across previously unseen environments.

cs.RO↗

arXiv2024

Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control

Objective: For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. Current control methods based on physiological signals such as electromyography (EMG) are prone to yielding poor inference outcomes due to motion artifacts, muscle fatigue, and many more. Vision sensors are a major source of information about the environment state and can play a vital role in inferring feasible and intended gestures. However, visual evidence is also susceptible to its own artifacts, most often due to object occlusion, lighting changes, etc. Multimodal evidence fusion using physiological and vision sensor measurements is a natural approach due to the complementary strengths of these modalities. Methods: In this paper, we present a Bayesian evidence fusion framework for grasp intent inference using eye-view video, eye-gaze, and EMG from the forearm processed by neural network models. We analyze individual and fused performance as a function of time as the hand approaches the object to grasp it. For this purpose, we have also developed novel data processing and augmentation techniques to train neural network components. Results: Our results indicate that, on average, fusion improves the instantaneous upcoming grasp type classification accuracy while in the reaching phase by 13.66% and 14.8%, relative to EMG (81.64% non-fused) and visual evidence (80.5% non-fused) individually, resulting in an overall fusion accuracy of 95.3%. Conclusion: Our experimental data analyses demonstrate that EMG and visual evidence show complementary strengths, and as a consequence, fusion of multimodal evidence can outperform each individual evidence modality at any given time.

cs.RO↗

arXiv2024

Design and Realization of a Benchmarking Testbed for Evaluating Autonomous Platooning Algorithms

Autonomous vehicle platoons present near- and long-term opportunities to enhance operational efficiencies and save lives. The past 30 years have seen rapid development in the autonomous driving space, enabling new technologies that will alleviate the strain placed on human drivers and reduce vehicle emissions. This paper introduces a testbed for evaluating and benchmarking platooning algorithms on 1/10th scale vehicles with onboard sensors. To demonstrate the testbed's utility, we evaluate three algorithms, linear feedback and two variations of distributed model predictive control, and compare their results on a typical platooning scenario where the lead vehicle tracks a reference trajectory that changes speed multiple times. We validate our algorithms in simulation to analyze the performance as the platoon size increases, and find that the distributed model predictive control algorithms outperform linear feedback on hardware and in simulation.

cs.RO↗

arXiv2024

E(2)-Equivariant Graph Planning for Navigation

Learning for robot navigation presents a critical and challenging task. The scarcity and costliness of real-world datasets necessitate efficient learning approaches. In this letter, we exploit Euclidean symmetry in planning for 2D navigation, which originates from Euclidean transformations between reference frames and enables parameter sharing. To address the challenges of unstructured environments, we formulate the navigation problem as planning on a geometric graph and develop an equivariant message passing network to perform value iteration. Furthermore, to handle multi-camera input, we propose a learnable equivariant layer to lift features to a desired space. We conduct comprehensive evaluations across five diverse tasks encompassing structured and unstructured environments, along with maps of known and unknown, given point goals or semantic goals. Our experiments confirm the substantial benefits on training efficiency, stability, and generalization. More details can be found at the project website: https://lhy.xyz/e2-planning/.

cs.RO↗

arXiv2024

Analysis and Perspectives on the ANA Avatar XPRIZE Competition

The ANA Avatar XPRIZE was a four-year competition to develop a robotic "avatar" system to allow a human operator to sense, communicate, and act in a remote environment as though physically present. The competition featured a unique requirement that judges would operate the avatars after less than one hour of training on the human-machine interfaces, and avatar systems were judged on both objective and subjective scoring metrics. This paper presents a unified summary and analysis of the competition from technical, judging, and organizational perspectives. We study the use of telerobotics technologies and innovations pursued by the competing teams in their avatar systems, and correlate the use of these technologies with judges' task performance and subjective survey ratings. It also summarizes perspectives from team leads, judges, and organizers about the competition's execution and impact to inform the future development of telerobotics and telepresence.

cs.RO↗

arXiv2023

Shared Affordance-awareness via Augmented Reality for Proactive Assistance in Human-robot Collaboration

Enabling humans and robots to collaborate effectively requires purposeful communication and an understanding of each other's affordances. Prior work in human-robot collaboration has incorporated knowledge of human affordances, i.e., their action possibilities in the current context, into autonomous robot decision-making. This "affordance awareness" is especially promising for service robots that need to know when and how to assist a person that cannot independently complete a task. However, robots still fall short in performing many common tasks autonomously. In this work-in-progress paper, we propose an augmented reality (AR) framework that bridges the gap in an assistive robot's capabilities by actively engaging with a human through a shared affordance-awareness representation. Leveraging the different perspectives from a human wearing an AR headset and a robot's equipped sensors, we can build a perceptual representation of the shared environment and model regions of respective agent affordances. The AR interface can also allow both agents to communicate affordances with one another, as well as prompt for assistance when attempting to perform an action outside their affordance region. This paper presents the main components of the proposed framework and discusses its potential through a domestic cleaning task experiment.

cs.RO↗

arXiv2023

TRASH: Tandem Rover and Aerial Scrap Harvester

Addressing the challenge of roadside litter in the United States, which has traditionally relied on costly and ineffective manual cleanup methods, this paper presents an autonomous multi-robot system for highway litter monitoring and collection. Our solution integrates an aerial vehicle to scan and gather data across highway stretches with a terrestrial robot equipped with a Convolutional Neural Network (CNN) for litter detection and mapping. Upon detecting litter, the ground robot navigates to each pinpointed location, re-assesses the vicinity, and employs a "greedy pickup" approach to address potential mapping inaccuracies or litter misplacements. Through simulation studies and real-world robotic trials, this work highlights the potential of our proposed system for highway cleanliness and management in the context of Robotics, Automation, and Artificial Intelligence

cs.RO↗

arXiv2023

Mobile MoCap: Retroreflector Localization On-The-Go

Motion capture through tracking retroreflectors obtains highly accurate pose estimation, which is frequently used in robotics. Unlike commercial motion capture systems, fiducial marker-based tracking methods, such as AprilTags, can perform relative localization without requiring a static camera setup. However, popular pose estimation methods based on fiducial markers have lower localization accuracy than commercial motion capture systems. We propose Mobile MoCap, a system that utilizes inexpensive near-infrared cameras for accurate relative localization even while in motion. We present a retroreflector feature detector that performs 6-DoF (six degrees-of-freedom) tracking and operates with minimal camera exposure times to reduce motion blur. To evaluate the proposed localization technique while in motion, we mount our Mobile MoCap system, as well as an RGB camera to benchmark against fiducial markers, onto a precision-controlled linear rail and servo. The fiducial marker approach employs AprilTags, which are pervasively used for localization in robotics. We evaluate the two systems at varying distances, marker viewing angles, and relative velocities. Across all experimental conditions, our stereo-based Mobile MoCap system obtains higher position and orientation accuracy than the fiducial approach. The code for Mobile MoCap is implemented in ROS 2 and made publicly available at https://github.com/RIVeR-Lab/mobile_mocap.

cs.RO↗

arXiv2023

StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks

Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the computational constraints for real-time feedback. This paper proposes a computationally efficient method that employs a deep neural network to detect occupancy from stereo images directly. Instead of learning the point cloud correspondence from the stereo data, our approach extracts the compact obstacle distribution based on volumetric representations. In addition, we prune the computation of safety irrelevant spaces in a coarse-to-fine manner based on octrees generated by the decoder. As a result, we achieve real-time performance on the onboard computer (NVIDIA Jetson TX2). Our approach detects obstacles accurately in the range of 32 meters and achieves better IoU (Intersection over Union) and CD (Chamfer Distance) scores with only 2% of the computation cost of the state-of-the-art stereo model. Furthermore, we validate our method's robustness and real-world feasibility through autonomous navigation experiments with a real robot. Hence, our work contributes toward closing the gap between the stereo-based system in robot perception and state-of-the-art stereo models in computer vision. To counter the scarcity of high-quality real-world indoor stereo datasets, we collect a 1.36 hours stereo dataset with a mobile robot which is used to fine-tune our model. The dataset, the code, and further details including additional visualizations are available at https://lhy.xyz/stereovoxelnet

cs.RO↗

arXiv2022

Contact-Implicit Planning and Control for Non-Prehensile Manipulation Using State-Triggered Constraints

We present a contact-implicit planning approach that can generate contact-interaction trajectories for non-prehensile manipulation problems without tuning or a tailored initial guess and with high success rates. This is achieved by leveraging the concept of state-triggered constraints (STCs) to capture the hybrid dynamics induced by discrete contact modes without explicitly reasoning about the combinatorics. STCs enable triggering arbitrary constraints by a strict inequality condition in a continuous way. We first use STCs to develop an automatic contact constraint activation method to minimize the effective constraint space based on the utility of contact candidates for a given task. Then, we introduce a re-formulation of the Coulomb friction model based on STCs that is more efficient for the discovery of tangential forces than the well-studied complementarity constraints-based approach. Last, we include the proposed friction model in the planning and control of quasi-static planar pushing. The performance of the STC-based contact activation and friction methods is evaluated by extensive simulation experiments in a dynamic pushing scenario. The results demonstrate that our methods outperform the baselines based on complementarity constraints with a significant decrease in the planning time and a higher success rate. We then compare the proposed quasi-static pushing controller against a mixed-integer programming-based approach in simulation and find that our method is computationally more efficient and provides a better tracking accuracy, with the added benefit of not requiring an initial control trajectory. Finally, we present hardware experiments demonstrating the usability of our framework in executing complex trajectories in real-time even with a low-accuracy tracking system.

cs.RO↗

arXiv2022

Pregrasp Object Material Classification by a Novel Gripper Design with Integrated Spectroscopy

Robots benefit from being able to classify objects they interact with or manipulate based on their material properties. This capability ensures fine manipulation of complex objects through proper grasp pose and force selection. Prior work has focused on haptic or visual processing to determine material type at grasp time. In this work, we introduce a novel parallel robot gripper design and a method for collecting spectral readings and visual images from within the gripper finger. We train a nonlinear Support Vector Machine (SVM) that can classify the material of the object about to be grasped through recursive estimation, with increasing confidence as the distance from the finger tips to the object decreases. In order to validate the hardware design and classification method, we collect samples from 16 real and fake fruit varieties (composed of polystyrene/plastic) resulting in a dataset containing spectral curves, scene images, and high-resolution texture images as the objects are grasped, lifted, and released. Our modeling method demonstrates an accuracy of 96.4% in classifying objects in a 32 class decision problem. This represents a performance improvement by 29.4% over the state of the art computer vision algorithms at distinguishing between visually similar materials. In contrast to prior work, our recursive estimation model accounts for increasing spectral signal strength and allows for decisions to be made as the gripper approaches an object. We conclude that spectroscopy is a promising sensing modality for enabling robots to not only classify grasped objects but also understand their underlying material composition.

cs.RO↗

arXiv2022

Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation

Object pose estimation methods allow finding locations of objects in unstructured environments. This is a highly desired skill for autonomous robot manipulation as robots need to estimate the precise poses of the objects in order to manipulate them. In this paper, we investigate the problems of tactile pose estimation and manipulation for category-level objects. Our proposed method uses a Bayes filter with a learned tactile observation model and a deterministic motion model. Later, we train policies using deep reinforcement learning where the agents use the belief estimation from the Bayes filter. Our models are trained in simulation and transferred to the real world. We analyze the reliability and the performance of our framework through a series of simulated and real-world experiments and compare our method to the baseline work. Our results show that the learned tactile observation model can localize the pose of novel objects at 2-mm and 1-degree resolution for position and orientation, respectively. Furthermore, we experiment on a bottle opening task where the gripper needs to reach the desired grasp state.

cs.RO↗

arXiv2021

End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning

State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues. As a workaround, researchers have been looking into integrating EMG with other signals, often in an ad hoc manner. In this paper, we are presenting a method for end-to-end training of a policy for human-in-the-loop robot grasping on real reaching trajectories. For this purpose we use Reinforcement Learning (RL) and Imitation Learning (IL) in DEXTRON (DEXTerity enviRONment), a stochastic simulation environment with real human trajectories that are augmented and selected using a Monte Carlo (MC) simulation method. We also offer a success model which once trained on the expert policy data and the RL policy roll-out transitions, can provide transparency to how the deep policy works and when it is probably going to fail.

cs.RO↗

arXiv2021

Affordance-Based Mobile Robot Navigation Among Movable Obstacles

Avoiding obstacles in the perceived world has been the classical approach to autonomous mobile robot navigation. However, this usually leads to unnatural and inefficient motions that significantly differ from the way humans move in tight and dynamic spaces, as we do not refrain interacting with the environment around us when necessary. Inspired by this observation, we propose a framework for autonomous robot navigation among movable obstacles (NAMO) that is based on the theory of affordances and contact-implicit motion planning. We consider a realistic scenario in which a mobile service robot negotiates unknown obstacles in the environment while navigating to a goal state. An affordance extraction procedure is performed for novel obstacles to detect their movability, and a contact-implicit trajectory optimization method is used to enable the robot to interact with movable obstacles to improve the task performance or to complete an otherwise infeasible task. We demonstrate the performance of the proposed framework by hardware experiments with Toyota's Human Support Robot.

cs.RO↗

arXiv2020

Learning Bayes Filter Models for Tactile Localization

Localizing and tracking the pose of robotic grippers are necessary skills for manipulation tasks. However, the manipulators with imprecise kinematic models (e.g. low-cost arms) or manipulators with unknown world coordinates (e.g. poor camera-arm calibration) cannot locate the gripper with respect to the world. In these circumstances, we can leverage tactile feedback between the gripper and the environment. In this paper, we present learnable Bayes filter models that can localize robotic grippers using tactile feedback. We propose a novel observation model that conditions the tactile feedback on visual maps of the environment along with a motion model to recursively estimate the gripper's location. Our models are trained in simulation with self-supervision and transferred to the real world. Our method is evaluated on a tabletop localization task in which the gripper interacts with objects. We report results in simulation and on a real robot, generalizing over different sizes, shapes, and configurations of the objects.

cs.RO↗

arXiv2020

Model-Based Manipulation of Linear Flexible Objects with Visual Curvature Feedback

Manipulation of deformable objects is a desired skill in making robots ubiquitous in manufacturing, service, healthcare, and security. Deformable objects are common in our daily lives, e.g., wires, clothes, bed sheets, etc., and are significantly more difficult to model than rigid objects. In this study, we investigate vision-based manipulation of linear flexible objects such as cables. We propose a geometric modeling method that is based on visual feedback to develop a general representation of the linear flexible object that is subject to gravity. The model characterizes the shape of the object by combining the curvatures on two projection planes. In this approach, we achieve tracking of the position and orientation (pose) of a cable-like object, the pose of its tip, and the pose of the selected grasp point on the object, which enables closed-loop manipulation of the object. We demonstrate the feasibility of our approach by completing the Plug Task used in the 2015 DARPA Robotics Challenge Finals, which involves unplugging a power cable from one socket and plugging it into another. Experiments show that we can successfully complete the task autonomously within 30 seconds.

cs.RO↗

arXiv2020

Tuning-Free Contact-Implicit Trajectory Optimization

We present a contact-implicit trajectory optimization framework that can plan contact-interaction trajectories for different robot architectures and tasks using a trivial initial guess and without requiring any parameter tuning. This is achieved by using a relaxed contact model along with an automatic penalty adjustment loop for suppressing the relaxation. Moreover, the structure of the problem enables us to exploit the contact information implied by the use of relaxation in the previous iteration, such that the solution is explicitly improved with little computational overhead. We test the proposed approach in simulation experiments for non-prehensile manipulation using a 7-DOF arm and a mobile robot and for planar locomotion using a humanoid-like robot in zero gravity. The results demonstrate that our method provides an out-of-the-box solution with good performance for a wide range of applications.

cs.RO↗

arXiv2020

Sim2Real2Sim: Bridging the Gap Between Simulation and Real-World in Flexible Object Manipulation

This paper addresses a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim) to bridge the gap between simulation and real-world, and automate a flexible object manipulation task. This strategy consists of three steps: (1) using the rough environment with the estimated models to develop the methods to complete the manipulation task in the simulation; (2) applying the methods from simulation to real-world and comparing their performance; (3) updating the models and methods in simulation based on the differences between the real world and the simulation. The Plug Task from the 2015 DARPA Robotics Challenge Finals is chosen to evaluate our Sim2Real2Sim strategy. A new identification approach for building the model of the linear flexible objects is derived from real-world to simulation. The automation of the DRC plug task in both simulation and real-world proves the success of the Sim2Real2Sim strategy. Numerical experiments are implemented to validate the simulated model.

cs.RO↗