Tiansheng Gu - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Tiansheng Gu

Publications

2

Citations

0

Publications by Year

20132025

Co-Authors:

Zhongjie BaFan BaiZongkai JiYimu JiBing Jiang

Show All Co-Authors (14)

Tiansheng Gu

Affiliation

School of Computer Science and Technology

Nanjing University of Posts and Telecommunications

Nanjing, China

Publication Topics

Active Defense,
Ag Nanoparticles,
Anomaly Score,
Anti-reflective Properties,
Arbitration,
Attack Methods,
Attack Success,
Convolutional Neural Network,
Coordinated Attacks,
Cybersecurity,
Device Output,
Error Probability

Biography

Tiansheng Gu received the BS degree in network engineering from the Nanjing University of Information Science & Technology (NUIST), in 2022. He is currently working towards the master's degree with the School of Computer Science, NJUPT. His current research is mimic defense in network security.(Based on document published on 7 February 2025).

Publications

2

Citations

0

Publications by Year

20132025

Co-Authors:

Zhongjie Ba
Fan Bai
Zongkai Ji
Yimu Ji
Bing Jiang

Show All Co-Authors (14)

Author's Published Works

Search History

Showing 1-25 of 38 results

Conferences (26)

Journals (12)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Aaron M. Johnson(37)
Nathan J. Kong(6)
J. Joe Payne(5)
James Zhu(5)
Joseph Norby(5)
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA(21)
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA(5)
Electrical and Systems Engineering Department, University of Pennsylvania, Philadelphia, PA, USA(3)
Jet Propulsion Laboratory, California Institute of Technology(3)
Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA(2)
IEEE Robotics and Automation Letters(5)
2022 International Conference on Robotics and Automation (ICRA)(4)
2024 IEEE International Conference on Robotics and Automation (ICRA)(4)
IEEE Transactions on Robotics(4)
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(3)
IEEE(37)
FRPS(1)
Media(14)
Philadelphia, PA, USA(4)
Yokohama, Japan(4)
Detroit, MI, USA(3)
Kyoto, Japan(3)
Austin, TX, USA(2)
Legged Robots(14)
Actuator(10)
Hybrid System(10)
Trajectory Optimization(10)
Ground Reaction Force(8)

Select All on Page

Sort By

Results

Hybrid Iterative Linear Quadratic Estimation: Optimal Estimation for Hybrid Systems

J. Joe Payne;James Zhu;Nathan J. Kong;Aaron M. Johnson

IEEE Robotics and Automation Letters

Year: 2025 | Volume: 10, Issue: 4 | Journal Article |

HTML

In this letter we present Hybrid iterative Linear Quadratic Estimation (HiLQE), an optimization based offline state estimation algorithm for hybrid dynamical systems. We utilize the saltation matrix, a first order approximation of the variational update through an event driven hybrid transition, to calculate gradient information through hybrid events in the backward pass of an iterative linear qua...Show More

Hybrid Iterative Linear Quadratic Estimation: Optimal Estimation for Hybrid Systems

J. Joe Payne;James Zhu;Nathan J. Kong;Aaron M. Johnson

IEEE Robotics and Automation Letters

Year: 2025 | Volume: 10, Issue: 4 | Journal Article |

Saltation Matrices: The Essential Tool for Linearizing Hybrid Dynamical Systems

Nathan J. Kong;J. Joe Payne;James Zhu;Aaron M. Johnson

Proceedings of the IEEE

Year: 2024 | Volume: 112, Issue: 6 | Journal Article |

Cited by: Papers (6)

HTML

Hybrid dynamical systems, i.e., systems that have both continuous and discrete states, are ubiquitous in engineering but are difficult to work with due to their discontinuous transitions. For example, a robot leg is able to exert very little control effort, while it is in the air compared to when it is on the ground. When the leg hits the ground, the penetrating velocity instantaneously collapses ...Show More

Saltation Matrices: The Essential Tool for Linearizing Hybrid Dynamical Systems

Nathan J. Kong;J. Joe Payne;James Zhu;Aaron M. Johnson

Proceedings of the IEEE

Year: 2024 | Volume: 112, Issue: 6 | Journal Article |

LORIS: A Lightweight Free-Climbing Robot for Extreme Terrain Exploration

Paul Nadan;Spencer Backus;Aaron M. Johnson

2024 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2024 | Conference Paper |

HTML

Climbing robots can investigate scientifically valuable sites that conventional rovers cannot access due to steep terrain features. Robots equipped with microspine grippers are particularly well-suited to ascending rocky cliff faces, but most existing designs are either large and slow or limited to relatively flat surfaces such as walls. We present a novel free-climbing robot to bridge this gap th...Show More

LORIS: A Lightweight Free-Climbing Robot for Extreme Terrain Exploration

Paul Nadan;Spencer Backus;Aaron M. Johnson

2024 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2024 | Conference Paper |

Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning

Ardalan Tajbakhsh;Lorenz T. Biegler;Aaron M. Johnson

2024 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

HTML

This paper presents a scalable multi-robot motion planning algorithm called Conflict-Based Model Predictive Control (CB-MPC). Inspired by Conflict-Based Search (CBS), the planner leverages a modified high-level conflict tree to efficiently resolve robot-robot conflicts in the continuous space, while reasoning about each agent’s kinematic and dynamic constraints and actuation limits using MPC as th...Show More

Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning

Ardalan Tajbakhsh;Lorenz T. Biegler;Aaron M. Johnson

2024 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2024 | Conference Paper |

Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Driving

Sean J. Wang;Honghao Zhu;Aaron M. Johnson

2024 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2024 | Conference Paper |

HTML

Autonomous off-road driving is challenging as unsafe actions may lead to catastrophic damage. As such, developing controllers in simulation is often desirable. However, robot dynamics in unstructured off-road environments can be highly complex and difficult to simulate accurately. Domain randomization addresses this problem by randomizing simulation dynamics to train policies that are robust towar...Show More

Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Driving

Sean J. Wang;Honghao Zhu;Aaron M. Johnson

2024 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2024 | Conference Paper |

Convergent iLQR for Safe Trajectory Planning and Control of Legged Robots

James Zhu;J. Joe Payne;Aaron M. Johnson

2024 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

HTML

In order to perform highly dynamic and agile maneuvers, legged robots typically spend time in underactuated domains (e.g. with feet off the ground) where the system has limited command of its acceleration and a constrained amount of time before transitioning to a new domain (e.g. foot touchdown). Meanwhile, these transitions can instantaneously change the system’s state, possibly causing perturbat...Show More

Convergent iLQR for Safe Trajectory Planning and Control of Legged Robots

James Zhu;J. Joe Payne;Aaron M. Johnson

2024 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2024 | Conference Paper |

Adaptive Complexity Model Predictive Control

Joseph Norby;Ardalan Tajbakhsh;Yanhao Yang;Aaron M. Johnson

IEEE Transactions on Robotics

Year: 2024 | Volume: 40 | Journal Article |

Cited by: Papers (2)

HTML

This work introduces a formulation of model predictive control (MPC), which adaptively reasons about the complexity of the model while maintaining feasibility and stability guarantees. Existing approaches often handle computational complexity by shortening prediction horizons or simplifying models, both of which can result in instability. Inspired by related approaches in behavioral economics, mot...Show More

Adaptive Complexity Model Predictive Control

Joseph Norby;Ardalan Tajbakhsh;Yanhao Yang;Aaron M. Johnson

IEEE Transactions on Robotics

Year: 2024 | Volume: 40 | Journal Article |

The Simplest Walking Robot: A Bipedal Robot with One Actuator and two Rigid Bodies

James Kyle;Justin K. Yim;Kendall Hart;Sarah Bergbreiter;Aaron M. Johnson

2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

HTML

We present the design and experimental results of the first 1-DOF, hip-actuated bipedal robot. While passive dynamic walking is simple by nature, many existing bipeds inspired by this form of walking are complex in control, mechanical design, or both. Our design using only two rigid bodies connected by a single motor aims to enable exploration of walking at smaller sizes where more complex designs...Show More

The Simplest Walking Robot: A Bipedal Robot with One Actuator and two Rigid Bodies

James Kyle;Justin K. Yim;Kendall Hart;Sarah Bergbreiter;Aaron M. Johnson

2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids)

Year: 2023 | Conference Paper |

Proprioception and Tail Control Enable Extreme Terrain Traversal by Quadruped Robots

Yanhao Yang;Joseph Norby;Justin K. Yim;Aaron M. Johnson

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2023 | Conference Paper |

Cited by: Papers (4)

HTML

Legged robots leverage ground contacts and the reaction forces they provide to achieve agile locomotion. However, uncertainty coupled with contact discontinuities can lead to failure, especially in real-world environments with unexpected height variations such as rocky hills or curbs. To enable dynamic traversal of extreme terrain, this work introduces 1) a proprioception-based gait planner for es...Show More

Proprioception and Tail Control Enable Extreme Terrain Traversal by Quadruped Robots

Yanhao Yang;Joseph Norby;Justin K. Yim;Aaron M. Johnson

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2023 | Conference Paper |

Staged Contact Optimization: Combining Contact-Implicit and Multi-Phase Hybrid Trajectory Optimization

Michael R. Turski;Joseph Norby;Aaron M. Johnson

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

HTML

Trajectory optimization problems for legged robots are commonly formulated with fixed contact schedules. These multi-phase Hybrid Trajectory Optimization (HTO) methods result in locally optimal trajectories, but the result depends heavily upon the predefined contact mode sequence. Contact-Implicit Optimization (CIO) offers a potential solution to this issue by allowing the contact mode to be deter...Show More

Staged Contact Optimization: Combining Contact-Implicit and Multi-Phase Hybrid Trajectory Optimization

Michael R. Turski;Joseph Norby;Aaron M. Johnson

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2023 | Conference Paper |

Proprioception and Reaction for Walking Among Entanglements

Justin K. Yim;Jiming Ren;David Ologan;Selvin Garcia Gonzalez;Aaron M. Johnson

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2023 | Conference Paper |

HTML

Entanglements like vines and branches in natural settings or cords and pipes in human spaces prevent mobile robots from accessing many environments. Legged robots should be effective in these settings, and more so than wheeled or tracked platforms, but naive controllers quickly become entangled and stuck. In this paper we present a method for proprioception aimed specifically at the task of sensin...Show More

Proprioception and Reaction for Walking Among Entanglements

Justin K. Yim;Jiming Ren;David Ologan;Selvin Garcia Gonzalez;Aaron M. Johnson

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2023 | Conference Paper |

Grounding Robot Navigation in Self-Defense Law

James Zhu;Anoushka Shrivastava;Aaron M. Johnson

2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

Year: 2023 | Conference Paper |

HTML

Robots operating in close proximity to humans rely heavily on human trust to successfully complete their tasks. But what are the real outcomes when this trust is violated? Self-defense law provides a framework for analyzing tangible failure scenarios that can inform the design of robots and their algorithms. Studying self-defense is particularly important for ground robots since they operate withi...Show More

Grounding Robot Navigation in Self-Defense Law

James Zhu;Anoushka Shrivastava;Aaron M. Johnson

2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

Year: 2023 | Conference Paper |

Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots

Nathan J. Kong;Chuanzheng Li;George Council;Aaron M. Johnson

IEEE Transactions on Robotics

Year: 2023 | Volume: 39, Issue: 6 | Journal Article |

Cited by: Papers (18)

HTML

Model predictive control (MPC) is a popular strategy for controlling robots but is difficult for systems with contact due to the complex nature of hybrid dynamics. To implement MPC for systems with contact, dynamic models are often simplified or contact sequences fixed in time in order to plan trajectories efficiently. In this work, we propose the hybrid iterative linear quadratic regulator (iLQR)...Show More

Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots

Nathan J. Kong;Chuanzheng Li;George Council;Aaron M. Johnson

IEEE Transactions on Robotics

Year: 2023 | Volume: 39, Issue: 6 | Journal Article |

Editorial: Special Issue on Robotics Collaborative Technology Alliance (RCTA) Program

Larry Matthies;Aaron M. Johnson;Monroe Kennedy;Cynthia Matuszek;Dilip G. Patel

Year: 2022 | Volume: 2 | Journal Article |

The growing technology investment in driverless cars, warehouse automation, human-service robots, and artificial intelligence applications in our daily lives is inspiring. However, the majority of this effort assumes a structured environment, which leaves significant, unsolved problems for autonomous robots operating in unstructured environments. Beginning in 2010, the U.S. Combat Capabilities Dev...Show More

Editorial: Special Issue on Robotics Collaborative Technology Alliance (RCTA) Program

Larry Matthies;Aaron M. Johnson;Monroe Kennedy;Cynthia Matuszek;Dilip G. Patel

Year: 2022 | Volume: 2 | Journal Article |

The Uncertainty Aware Salted Kalman Filter: State Estimation for Hybrid Systems with Uncertain Guards

J. Joe Payne;Nathan J. Kong;Aaron M. Johnson

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2022 | Conference Paper |

Cited by: Papers (6)

HTML

In this paper, we present a method for updating robotic state belief through contact with uncertain surfaces and apply this update to a Kalman filter for more accurate state estimation. Examining how guard surface uncertainty affects the time spent in each mode, we derive a novel guard saltation matrix- which maps perturbations prior to hybrid events to perturbations after - accounting for additio...Show More

The Uncertainty Aware Salted Kalman Filter: State Estimation for Hybrid Systems with Uncertain Guards

J. Joe Payne;Nathan J. Kong;Aaron M. Johnson

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2022 | Conference Paper |

Microspine Design for Additive Manufacturing

Paul Nadan;Dinesh K. Patel;Catherine Pavlov;Spencer Backus;Aaron M. Johnson

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

HTML

Microspine grippers allow robots to ascend steep rocky slopes and cliff faces, enabling scientific exploration of exposed strata on Earth and other solar system bodies. Historically, the Shape Deposition Manufacturing (SDM) process has been used to fabricate multi-material suspensions for load-sharing among multiple microspines. We instead apply the Hybrid Deposition Manufacturing (HDM) process to...Show More

Microspine Design for Additive Manufacturing

Paul Nadan;Dinesh K. Patel;Catherine Pavlov;Spencer Backus;Aaron M. Johnson

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2022 | Conference Paper |

Hybrid Event Shaping to Stabilize Periodic Hybrid Orbits

James Zhu;Nathan J. Kong;George Council;Aaron M. Johnson

2022 International Conference on Robotics and Automation (ICRA)

Year: 2022 | Conference Paper |

Cited by: Papers (6)

HTML

Many controllers for legged robotic systems leverage open- or closed-loop control at discrete hybrid events to enhance stability. These controllers appear in several well studied phenomena such as the Raibert stepping controller, paddle juggling, and swing leg retraction. This work introduces hybrid event shaping (HES): a generalized method for analyzing and designing stable hybrid event controlle...Show More

Hybrid Event Shaping to Stabilize Periodic Hybrid Orbits

James Zhu;Nathan J. Kong;George Council;Aaron M. Johnson

2022 International Conference on Robotics and Automation (ICRA)

Year: 2022 | Conference Paper |

Scalable Minimally Actuated Leg Extension Bipedal Walker Based on 3D Passive Dynamics

Sharfin Islam;Kamal Carter;Justin Yim;James Kyle;Sarah Bergbreiter;Aaron M. Johnson

2022 International Conference on Robotics and Automation (ICRA)

Year: 2022 | Conference Paper |

Cited by: Papers (6)

HTML

We present simplified 2D dynamic models of the 3D, passive dynamic inspired walking gait of a physical quasi-passive walking robot. Quasi-passive walkers are robots that integrate passive walking principles and some form of actuation. Our ultimate goal is to better understand the dynamics of actuated walking in order to create miniature, untethered, bipedal walking robots. At these smaller scales ...Show More

Scalable Minimally Actuated Leg Extension Bipedal Walker Based on 3D Passive Dynamics

Sharfin Islam;Kamal Carter;Justin Yim;James Kyle;Sarah Bergbreiter;Aaron M. Johnson

2022 International Conference on Robotics and Automation (ICRA)

Year: 2022 | Conference Paper |

Periodic SLAM: Using Cyclic Constraints to Improve the Performance of Visual-Inertial SLAM on Legged Robots

Hans Kumar;J. Joe Payne;Matthew Travers;Aaron M. Johnson;Howie Choset

2022 International Conference on Robotics and Automation (ICRA)

Year: 2022 | Conference Paper |

Cited by: Papers (3)

HTML

Methods for state estimation that rely on visual information are challenging on legged robots due to rapid changes in the viewing angle of onboard cameras. In this work, we show that by leveraging structure in the way that the robot locomotes, the accuracy of visual-inertial SLAM in these challenging scenarios can be increased. We present a method that takes advantage of the underlying periodic pr...Show More

Periodic SLAM: Using Cyclic Constraints to Improve the Performance of Visual-Inertial SLAM on Legged Robots

Hans Kumar;J. Joe Payne;Matthew Travers;Aaron M. Johnson;Howie Choset

2022 International Conference on Robotics and Automation (ICRA)

Year: 2022 | Conference Paper |

TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models

Samuel Triest;Matthew Sivaprakasam;Sean J. Wang;Wenshan Wang;Aaron M. Johnson;Sebastian Scherer

2022 International Conference on Robotics and Automation (ICRA)

Year: 2022 | Conference Paper |

Cited by: Papers (21)

HTML

We present TartanDrive, a large scale dataset for learning dynamics models for off-road driving. We collected a dataset of roughly 200,000 off-road driving interactions on a modified Yamaha Viking ATV with seven unique sensing modalities in diverse terrains. To the authors' knowledge, this is the largest real-world multi-modal off-road driving dataset, both in terms of number of interactions and s...Show More

TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models

Samuel Triest;Matthew Sivaprakasam;Sean J. Wang;Wenshan Wang;Aaron M. Johnson;Sebastian Scherer

2022 International Conference on Robotics and Automation (ICRA)

Year: 2022 | Conference Paper |

Contact-Implicit Direct Collocation With a Discontinuous Velocity State

Stacey Shield;Aaron M. Johnson;Amir Patel

IEEE Robotics and Automation Letters

Year: 2022 | Volume: 7, Issue: 2 | Journal Article |

Cited by: Papers (7)

HTML

Collisions between rigid bodies are impulsive events, meaning they create near-instantaneous, finite discontinuities in the velocity state of the dynamic system. If velocity is assumed to be continuous, that requirement may come into conflict with the desire for bodies to not interpenetrate, or with Coulomb friction (creating a condition known as the Painlevé paradox). This letter presents a conta...Show More

Contact-Implicit Direct Collocation With a Discontinuous Velocity State

Stacey Shield;Aaron M. Johnson;Amir Patel

IEEE Robotics and Automation Letters

Year: 2022 | Volume: 7, Issue: 2 | Journal Article |

iLQR for Piecewise-Smooth Hybrid Dynamical Systems

Nathan J. Kong;George Council;Aaron M. Johnson

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Cited by: Papers (13)

HTML

Trajectory optimization is a popular strategy for planning trajectories for robotic systems. However, many robotic tasks require changing contact conditions, which is difficult due to the hybrid nature of the dynamics. The optimal sequence and timing of these modes are typically not known ahead of time. In this work, we extend the Iterative Linear Quadratic Regulator (iLQR) method to a class of pi...Show More

iLQR for Piecewise-Smooth Hybrid Dynamical Systems

Nathan J. Kong;George Council;Aaron M. Johnson

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Enabling Dynamic Behaviors With Aerodynamic Drag in Lightweight Tails

Joseph Norby;Jun Yang Li;Cameron Selby;Amir Patel;Aaron M. Johnson

IEEE Transactions on Robotics

Year: 2021 | Volume: 37, Issue: 4 | Journal Article |

Cited by: Papers (21)

HTML

Many agile legged animals employ lightweight, furry tails to regulate orientation during running, leaping, and turning. Most robots attempting the same tasks either lack a tail or employ one with high inertia, which can induce impractical payload and energy costs. Inspired by nature's solution to this tradeoff, we explore the use of aerodynamic drag tails in reorientation tasks. In this article, w...Show More

Enabling Dynamic Behaviors With Aerodynamic Drag in Lightweight Tails

Joseph Norby;Jun Yang Li;Cameron Selby;Amir Patel;Aaron M. Johnson

IEEE Transactions on Robotics

Year: 2021 | Volume: 37, Issue: 4 | Journal Article |

Fast Global Motion Planning for Dynamic Legged Robots

Joseph Norby;Aaron M. Johnson

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2020 | Conference Paper |

Cited by: Papers (20)

HTML

This work presents a motion planning algorithm for legged robots capable of constructing long-horizon dynamic plans in real-time. Many existing methods use models that prohibit flight phases or even require static stability, while those that permit these dynamics often plan over short horizons or take minutes to compute. The algorithm presented here resolves these issues through a reduced-order dy...Show More

Fast Global Motion Planning for Dynamic Legged Robots

Joseph Norby;Aaron M. Johnson

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2020 | Conference Paper |

Contact Localization using Velocity Constraints

Sean Wang;Ankit Bhatia;Matthew T. Mason;Aaron M. Johnson

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2020 | Conference Paper |

Cited by: Papers (15)

HTML

Localizing contacts and collisions is an important aspect of failure detection and recovery for robots and can aid perception and exploration of the environment. Contrary to state-of-the-art methods that rely on forces and torques measured on the robot, this paper proposes a kinematic method for proprioceptive contact localization on compliant robots using velocity measurements. The method is vali...Show More

Contact Localization using Velocity Constraints

Sean Wang;Ankit Bhatia;Matthew T. Mason;Aaron M. Johnson

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2020 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.