Author name cluster

Taskin Padir

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28 papers

2 author rows

ICRA Conference 2025 Conference Paper

Data-Driven Sampling Based Stochastic MPC for Skid-Steer Mobile Robot Navigation

Ananya Trivedi
Sarvesh Prajapati
Anway Shirgaonkar
Mark Zolotas
Taskin Padir

Traditional approaches to motion modeling for skid-steer robots struggle to capture nonlinear tire-terrain dynamics, especially during high-speed maneuvers. In this paper, we tackle such nonlinearities by enhancing a dynamic unicycle model with Gaussian Process (GP) regression outputs. This enables us to develop an adaptive, uncertainty-informed navigation formulation. We solve the resultant stochastic optimal control problem using a chance-constrained Model Predictive Path Integral (MPPI) control method. This approach formulates obstacle avoidance and path-following as chance constraints, accounting for residual uncertainties from the GP to ensure safety and reliability in control. Leveraging GPU acceleration, we efficiently manage the non-convex nature of the problem, ensuring real-time performance. Our approach unifies path-following and obstacle avoidance across different terrains, unlike prior works which typically focus on one or the other. We compare our GP-MPPI method against unicycle and data-driven kinematic models within the MPPI framework. In simulations, our approach shows superior tracking accuracy and obstacle avoidance. We further validate our approach through hardware experiments on a skid-steer robot platform, demonstrating its effectiveness in high-speed navigation. The GPU implementation of the proposed method and supplementary video footage are available at https://stochasticmppi.github.io.

ICRA Conference 2025 Conference Paper

ViTa-Zero: Zero-shot Visuotactile Object 6D Pose Estimation

Hongyu Li 0003
James Akl
Srinath Sridhar 0002
Tye Brady
Taskin Padir

Object 6D pose estimation is a critical challenge in robotics, particularly for manipulation tasks. While prior research combining visual and tactile (visuotactile) information has shown promise, these approaches often struggle with generalization due to the limited availability of visuotactile data. In this paper, we introduce ViTa-Zero, a zero-shot visuotactile pose estimation framework. Our key innovation lies in leveraging a visual model as its backbone and performing feasibility checking and test-time optimization based on physical constraints derived from tactile and proprioceptive observations. Specifically, we model the gripper-object interaction as a spring-mass system, where tactile sensors induce attractive forces, and proprioception generates repulsive forces. We validate our framework through experiments on a real-world robot setup, demonstrating its effectiveness across representative visual backbones and manipulation scenarios, including grasping, object picking, and bimanual handover. Compared to the visual models, our approach overcomes some drastic failure modes while tracking the in-hand object pose. In our experiments, our approach shows an average increase of 55% in AUC of ADD-S and 60% in ADD, along with an 80% lower position error compared to FoundationPose.

ICRA Conference 2024 Conference Paper

A Probabilistic Motion Model for Skid-Steer Wheeled Mobile Robot Navigation on Off-Road Terrains

Ananya Trivedi
Mark Zolotas
Adeeb Abbas
Sarvesh Prajapati
Salah Bazzi
Taskin Padir

Skid-Steer Wheeled Mobile Robots (SSWMRs) are increasingly being used for off-road autonomy applications. When turning at high speeds, these robots tend to undergo significant skidding and slipping. In this work, using Gaussian Process Regression (GPR) and Sigma-Point Transforms, we estimate the non-linear effects of tire-terrain interaction on robot velocities in a probabilistic fashion. Using the mean estimates from GPR, we propose a data-driven dynamic motion model that is more accurate at predicting future robot poses than conventional kinematic motion models. By efficiently solving a convex optimization problem based on the history of past robot motion, the GPR augmented motion model generalizes to previously unseen terrain conditions. The output distribution from the proposed motion model can be used for local motion planning approaches, such as stochastic model predictive control, leveraging model uncertainty to make safe decisions. We validate our work on a benchmark real-world multi-terrain SSWMR dataset. Our results show that the model generalizes to three different terrains while significantly reducing errors in linear and angular motion predictions. As shown in the attached video, we perform a separate set of experiments on a physical robot to demonstrate the robustness of the proposed algorithm.

IROS Conference 2024 Conference Paper

A Voxel-Enabled Robotic Assistant for Omnidirectional Conveyance

Michael Angelo Carvajal
Katiso Mabulu
Muneer Lalji
James Flanagan
Rui Luo 0005
Samuel Hibbard
Tanav Chinthapatla
Rohan Bettadpur

Conventional bidirectional conveyance platforms use a flat translating belt or a series of spinning wheels or rollers to apply a shear force to payloads to move them. Wheel/roller-based conveyors in particular cannot double as a worktop when idle, do not support collision-free multi-object manipulation by default, and are not optimized to move objects that are either slippery or pliable—let alone both. This paper introduces a Voxel-Enabled Robotic Assistant (VERA), a network of intelligent table "partitions" whose topologically dynamic worktops enable omnidirectional conveyance; each partition is composed of a 2D array of "quadrants, " axisymmetric modules that can be hot-swapped for maintenance or repairs; each quadrant contains a 2D array of "cells, " unitary robotic submodules; each cell houses an independently controllable "voxel, " the motorized rotary element that conveys an overhead object. The efficacy of a VERA prototype was determined by evaluating waypoint error as a range of payloads were maneuvered between trajectory waypoints. By conveying both pliable and rigid payloads having slippery textures, the faceted voxels outperformed those augmented to mimic the circular-profiled wheels/rollers of competitor systems. VERA also successfully performed collision-free multi-object planar manipulations planned by its pathfinding algorithm. In light of these results, VERA emerges as a promising material handling platform for use in "Future of Work" settings as the need for multi-purpose collaborative industrial robots continues to grow.

ICRA Conference 2024 Conference Paper

HASHI: Highly Adaptable Seafood Handling Instrument for Manipulation in Industrial Settings

Austin Allison
Nathaniel Hanson
Sebastian Wicke
Taskin Padir

The seafood processing industry provides fertile ground for robotics to impact the future-of-work from multiple perspectives including productivity, worker safety, and quality of work life. The robotics research challenge in this domain is the realization of flexible and reliable manipulation of soft, deformable, slippery, spiky and scaly objects. In this paper, we propose a novel robot end effector, called HASHI, that employs chopstick-like appendages for precise and dexterous manipulation. This gripper is capable of in-hand manipulation by rotating its two constituent sticks relative to each other and offers control of objects in all three axes of rotation by imitating human use of chopsticks. HASHI delicately positions and orients food through embedded 6-axis force-torque sensors. We derive and validate the kinematic model for HASHI, as well as demonstrate grip force and torque readings from the sensorization of each chopstick. We also evaluate the versatility of HASHI through grasping trials of a variety of real and simulated food items with varying geometry, weight, and firmness.

IROS Conference 2024 Conference Paper

PROSPECT: Precision Robot Spectroscopy Exploration and Characterization Tool

Nathaniel Hanson
Gary Lvov
Vedant Rautela
Samuel Hibbard
Ethan Holand
Charles DiMarzio
Taskin Padir

Near Infrared (NIR) spectroscopy is widely used in industrial quality control and automation to test the purity and grade of items. In this research, we propose a novel sensorized end effector and acquisition strategy to capture spectral signatures from objects and register them with a 3D point cloud. Our methodology first takes a 3D scan of an object generated by a time-of-flight depth camera and decomposes the object into a series of planned viewpoints covering the surface. We generate motion plans for a robot manipulator and end-effector to visit these viewpoints while maintaining a fixed distance and surface normal. This process is enabled by the spherical motion of the end-effector and ensures maximal spectral signal quality. By continuously acquiring surface reflectance values as the end-effector scans the target object, the autonomous system develops a four-dimensional model of the target object: position in an R 3 coordinate frame, and a reflectance vector denoting the associated spectral signature. We demonstrate this system in building spectral-spatial object profiles of increasingly complex geometries. We show the proposed system and spectral acquisition planning produce more consistent spectral signals than naïve point scanning strategies. Our work represents a significant step towards high-resolution spectral-spatial sensor fusion for automated quality assessment.

IROS Conference 2024 Conference Paper

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

Hongyu Li 0003
Taskin Padir
Huaizu Jiang

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.

IROS Conference 2024 Conference Paper

User-customizable Shared Control for Robot Teleoperation via Virtual Reality

Rui Luo 0005
Mark Zolotas
Drake Moore
Taskin Padir

Shared control can ease and enhance a human operator’s ability to teleoperate robots, particularly for intricate tasks demanding fine control over multiple degrees of freedom. However, the arbitration process dictating how much autonomous assistance to administer in shared control can confuse novice operators and impede their understanding of the robot’s behavior. To overcome these adverse side-effects, we propose a novel formulation of shared control that enables operators to tailor the arbitration to their unique capabilities and preferences. Unlike prior approaches to "customizable" shared control where users could indirectly modify the latent parameters of the arbitration function by issuing a feedback command, we instead make these parameters observable and directly editable via a virtual reality (VR) interface. We present our user-customizable shared control method for a teleoperation task in SE(3), known as the buzz wire game. A user study is conducted with participants teleoperating a robotic arm in VR to complete the game. The experiment spanned two weeks per subject to investigate longitudinal trends. Our findings reveal that users allowed to interactively tune the arbitration parameters across trials generalize well to adaptations in the task, exhibiting improvements in precision and fluency over direct teleoperation and conventional shared control.

ICRA Conference 2023 Conference Paper

SLURP! Spectroscopy of Liquids Using Robot Pre-Touch Sensing

Nathaniel Hanson
Wesley Lewis
Kavya Puthuveetil
Donelle Furline
Akhil Padmanabha
Taskin Padir
Zackory Erickson

Liquids and granular media are pervasive throughout human environments. Their free-flowing nature causes people to constrain them into containers. We do so with thousands of different types of containers made out of different materials with varying sizes, shapes, and colors. In this work, we present a state-of-the-art sensing technique for robots to perceive what liquid is inside of an unknown container. We do so by integrating Visible to Near Infrared (VNIR) reflectance spectroscopy into a robot's end effector. We introduce a hierarchical model for inferring the material classes of both containers and internal contents given spectral measurements from two integrated spectrometers. To train these inference models, we capture and open source a dataset of spectral measurements from over 180 different combinations of containers and liquids. Our technique demonstrates over 85% accuracy in identifying 13 different liquids and granular media contained within 13 different containers. The sensitivity of our spectral readings allow our model to also identify the material composition of the containers themselves with 96% accuracy. Overall, VNIR spectroscopy presents a promising method to give household robots a general-purpose ability to infer the liquids inside of containers, without needing to open or manipulate the containers.

ICRA Conference 2023 Conference Paper

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

Hongyu Li 0003
Zhengang Li 0001
Neset Ünver Akmandor
Huaizu Jiang
Yanzhi Wang 0001
Taskin Padir

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/.

IROS Conference 2023 Conference Paper

Team Northeastern's Approach to ANA XPRIZE Avatar Final Testing: A Holistic Approach to Telepresence and Lessons Learned

Rui Luo 0005
Chunpeng Wang
Colin Keil
David Nguyen
Henry Mayne
Stephen Alt
Eric Schwarm
Evelyn Mendoza

This paper reports on Team Northeastern's Avatar system for telepresence, and our holistic approach to meet the ANA Avatar XPRIZE Final testing task requirements. The system features a dual-arm configuration with hydraulically actuated glove-gripper pair for haptic force feedback. Our proposed Avatar system was evaluated in the ANA Avatar XPRIZE Finals and completed all 10 tasks, scored 14. 5 points out of 15. 0, and received the 3rd Place Award. We provide the details of improvements over our first generation Avatar, covering manipulation, perception, locomotion, power, network, and controller design. We also extensively discuss the major lessons learned during our participation in the competition.

IROS Conference 2022 Conference Paper

Deep Reinforcement Learning based Robot Navigation in Dynamic Environments using Occupancy Values of Motion Primitives

Neset Ünver Akmandor
Hongyu Li 0003
Gary Lvov
Eric Dusel
Taskin Padir

This paper presents a Deep Reinforcement Learning based navigation approach in which we define the occu-pancy observations as heuristic evaluations of motion primitives, rather than using raw sensor data. Our method enables fast mapping of the occupancy data, generated by multi-sensor fusion, into trajectory values in 3D workspace. The computationally efficient trajectory evaluation allows dense sampling of the action space. We utilize our occupancy observations in different data structures to analyze their effects on both training process and navigation performance. We train and test our methodology on two different robots within challenging physics-based simulation environments including static and dy-namic obstacles. We benchmark our occupancy representations with other conventional data structures from state-of-the-art methods. The trained navigation policies are also validated successfully with physical robots in dynamic environments. The results show that our method not only decreases the required training time but also improves the navigation performance as compared to other occupancy representations. The open-source implementation of our work and all related info are available at https://github.com/RIVeR-Lab/tentabot.

AAMAS Conference 2022 Conference Paper

Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation

Tarik Kelestemur
Robert Platt
Taskin Padir

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.

IROS Conference 2022 Conference Paper

Towards Robot Avatars: Systems and Methods for Teleinteraction at Avatar XPRIZE Semi-Finals

Rui Luo 0005
Chunpeng Wang
Eric Schwarm
Colin Keil
Evelyn Mendoza
Pushyami Kaveti
Stephen Alt
Hanumant Singh

There has been a drastic shift to remote interaction for professional, industrial and personal interactions. Improving the overall quality of these interactions by removing any sense of distance between the users is the ultimate goal. Video conferencing has been widely adopted as an improvement to audio-only interactions. Having added visuals to audio communication, the next frontier is to add physical interaction to this remote communication. In this paper, we present an avatar system with the aim of tackling these necessities. The proposed system includes both hardware and software designs to ensure a real-time telemanipulation experience with tactile force feedback. We present a coupled hydrostatic actuated gripper and glove with high system bandwidth to reduce the inherent latency of the mechanical system. To account for latency over the network, the wave variable based method is adopted to maintain the stability of the closed-loop gripper control even under hundreds of milliseconds of delay. A bidirectional audiovisual communication system comprised of off-the-shelf hardware and software is incorporated to allow realtime conversation between the operator and the recipient for collaborative tasks. the proposed system has been validated in lab experiments and the global ana avatar xprize challenge semifinal.

IROS Conference 2022 Conference Paper

VAST: Visual and Spectral Terrain Classification in Unstructured Multi-Class Environments

Nathaniel Hanson
Michael Shaham
Deniz Erdogmus
Taskin Padir

Terrain classification is a challenging task for robots operating in unstructured environments. Existing classification methods make simplifying assumptions, such as a reduced number of classes, clearly segmentable roads, or good lighting conditions, and focus primarily on one sensor type. These assumptions do not translate well to off-road vehicles, which operate in varying terrain conditions. To provide mobile robots with the capability to identify the terrain being traversed and avoid undesirable surface types, we propose a multimodal sensor suite capable of classifying different terrains. We capture high resolution macro images of surface texture, spectral reflectance curves, and localization data from a 9 degrees of freedom (DOF) inertial measurement unit (IMU) on 11 different terrains at different times of day. Using this dataset, we train individual neural networks on each of the modalities, and then combine their outputs in a fusion network. The fused network achieved an accuracy of 99. 98% percent on the test set, exceeding the results of the best individual network component by 0. 98%. We conclude that a combination of visual, spectral, and IMU data provides meaningful improvement over state of the art in terrain classification approaches. The data created for this research is available at https://github.com/RIVeR-Lab/vast_data.

ICRA Conference 2021 Conference Paper

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

Mohammadreza Sharif
Deniz Erdogmus
Christopher Amato
Taskin Padir

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.

IROS Conference 2021 Conference Paper

Policy Learning for Visually Conditioned Tactile Manipulation

Tarik Kelestemur
Taskin Padir
Robert Platt 0001

Recent work on robot learning with visual observations has shown great success in solving many manipulation tasks. While visual observations contain rich information about the environment and the robot, they can be unreliable in the presence of visual noise or occlusions. In these cases, we can leverage tactile observations generated by the interaction between the robot and the environment. In this paper, we propose a framework for learning manipulation policies that fuse visual and tactile feedback. The control problems considered in this work are to localize a gripper with respect to the environment image and navigate to desired states. Our method uses a learned Bayes filter to estimate the state of a gripper by conditioning the tactile observations on the environment image. We use deep reinforcement learning for solving the localization and navigation problems provided with the belief of the gripper’s state and the environment image. We compare our method against two baselines where the agent uses tactile observation directly with a recurrent neural network or uses a point estimate of the state instead of the full belief state. We also transfer the policies to the real world and validate them on a physical robot.

IROS Conference 2021 Conference Paper

Telemanipulation via Virtual Reality Interfaces with Enhanced Environment Models

Murphy Wonsick
Tarik Kelestemur
Stephen Alt
Taskin Padir

Extreme environments, such as search and rescue missions, defusing bombs, or exploring extraterrestrial planets, are unsafe environments for humans to be in. Robots enable humans to explore and interact in these environments through remote presence and teleoperation and virtual reality provides a medium to create immersive and easy-to-use teleoperation interfaces. However, current virtual reality interfaces are still very limited in their capabilities. In this work, we aim to advance robot teleoperation virtual reality interfaces by developing an environment reconstruction methodology capable of recognizing objects in a robot’s environment and rendering high fidelity models inside a virtual reality headset. We compare our proposed environment reconstruction method against traditional point cloud streaming by having operators plan waypoint trajectories to accomplish a pick-and-place task. Overall, our results show that users find our environment reconstruction method more usable and less cognitive work compared to raw point cloud streaming.

IROS Conference 2020 Conference Paper

Affordance-Based Mobile Robot Navigation Among Movable Obstacles

Maozhen Wang
Rui Luo 0005
Aykut Özgün Önol
Taskin Padir

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.

IROS Conference 2020 Conference Paper

Learning Bayes Filter Models for Tactile Localization

Tarik Kelestemur
Colin Keil
John P. Whitney
Robert Platt 0001
Taskin Padir

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.

ICRA Conference 2020 Conference Paper

Tuning-Free Contact-Implicit Trajectory Optimization

Aykut Özgün Önol
Radu Corcodel
Philip Long
Taskin Padir

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.

ICRA Conference 2019 Conference Paper

Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification

Aykut Özgün Önol
Philip Long
Taskin Padir

In this paper, we propose a contact-implicit trajectory optimization (CITO) method based on a variable smooth contact model (VSCM) and successive convexification (SCvx). The VSCM facilitates the convergence of gradient-based optimization without compromising physical fidelity. On the other hand, the proposed SCvx-based approach combines the advantages of direct and shooting methods for CITO. For evaluations, we consider non-prehensile manipulation tasks. The proposed method is compared to a version based on iterative linear quadratic regulator (iLQR) on a planar example. The results demonstrate that both methods can find physically-consistent motions that complete the tasks without a meaningful initial guess owing to the VSCM. The proposed SCvx-based method outperforms the iLQR-based method in terms of convergence, computation time, and the quality of motions found. Finally, the proposed SCvx-based method is tested on a standard robot platform and shown to perform efficiently for a real-world application.

ICRA Conference 2019 Conference Paper

optimization-Based Human-in-the-Loop Manipulation Using Joint Space Polytopes

Philip Long
Tarik Kelestemur
Aykut Özgün Önol
Taskin Padir

This paper presents a new method of maximizing the free space for a robot operating in a constrained environment under operator supervision. The objective is to make the resulting trajectories more robust to operator commands and/or changes in the environment. To represent the volume of free space, the constrained manipulability polytopes are used. These polytopes embed the distance to obstacles, the distance to joint limits and the distance to singular configurations. The volume of the resulting Cartesian polyhedron is used in an optimization-based motion planner to create the trajectories. Additionally, we show how fast collision-free inverse kinematic solutions can be obtained by exploiting the pre-computed inequality constraints. The proposed algorithm is validated in simulation and experimentally.

IROS Conference 2018 Conference Paper

A Comparative Analysis of Contact Models in Trajectory Optimization for Manipulation

Aykut Özgün Önol
Philip Long
Taskin Padir

In this paper, we analyze the effects of contact models on contact-implicit trajectory optimization for manipulation. We consider three different approaches: (1)a contact model that is based on complementarity constraints, (2)a smooth contact model, and our proposed method (3) a variable smooth contact model. We compare these models in simulation in terms of physical accuracy, quality of motions, and computation time. In each case, the optimization process is initialized by setting all torque variables to zero, namely, without a meaningful initial guess. For simulations, we consider a pushing task with varying complexity for a 7 degrees-of-freedom robot arm. Our results demonstrate that the optimization based on the proposed variable smooth contact model provides a good trade-off between the physical fidelity and quality of motions at the cost of increased computation time.

IROS Conference 2018 Conference Paper

A Novel Shared Position Control Method for Robot Navigation Via Low Throughput Human-Machine Interfaces

Dmitry A. Sinyukov
Taskin Padir

In this paper, we analyze systems with low throughput human-machine interfaces (such as a brain-computer interface, single switch interface) from the controls perspective. We develop some principles for performance improvement in such systems based on the parallelization of inference and robot motion. The proposed principles are used to design a novel shared position control to navigate a circular massless holonomic robot in a known environment. The system is implemented in simulation and integrated with a real robotic wheelchair. Robot experiments demonstrated the viability of the proposed navigation method in various modes of operation.

IROS Conference 2017 Conference Paper

Anytime multi-task motion planning for humanoid robots

Xianchao Long
Murphy Wonsick
Velin D. Dimitrov
Taskin Padir

This paper introduces an anytime synthesized motion planning algorithm for humanoid robots unifying locomotion and manipulation planning. It generates an entire set of motions to finish specific tasks in an environment containing obstacles by exploiting a powerful inverse kinematics (IK) engine. The IK engine can compute solutions allowing the robot to reposition its feet for meeting the task requirements. The presented planning algorithm has two primary beneficial capabilities. First, it is capable of generating a motion plan to complete a task handling multiple ordered or unordered actions. Second, it produces an initial solution very quickly, and then searches for the opportunity to improve the the solution during execution. The performance of the proposed algorithm is evaluated on the NASA-JSC Valkyrie humanoid robot by demonstrating an object pick up task in simulation and a box pick-and-place task in the real world.

ICRA Conference 2017 Conference Paper

CWave: High-performance single-source any-angle path planning on a grid

Dmitry A. Sinyukov
Taskin Padir

Path planning on a 2D-grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid. Single-source path planning is a modified problem which asks to find distances from a given point to all other points on the map. A high-performance algorithm for single-source any-angle path planning on a grid that we named CWave is proposed in this work. “Any-angle” attribute of a path planning algorithm implies that such algorithm can find paths which may include any angle segments, as opposed to standard A* on an 8-connected graph, the path can turn with 45°-increments only. The key idea of the presented algorithm is that it does not represent the grid as a graph and uses discrete geometric primitives to define the wave front. In its purest form, CWave requires for computation only integer arithmetics and multiplication by two, but can accumulate the distance error at turning points. A modified version of CWave with minimal usage of floating-point calculations is also developed. It allows to eliminate any accumulative errors which is proven mathematically and experimentally on several maps. The performance of the algorithm on three maps is demonstrated to be significantly faster than that of Theta*, Lazy Theta* and Field A* adapted for single-source planning. The limitations of the current implementations of the algorithm as well as potential improvements are discussed.

IROS Conference 2016 Conference Paper

Template-based human supervised robot task programming

Xianchao Long
Taskin Padir

Motions of a robot interacting with its environment can be described by a set of constraints. This paper introduces an approach, called motion template, which can quickly program and compose the constraints for the motion planner to generate the trajectory. Two types of motion templates, grasp and turn, are specifically described to explain the details of the technique. The reusability and shareability properties of the motion template are demonstrated using a variety of the motion planning applications across different robot platforms. A motion template framework is used to implement the motion template with the trajectory optimization.