41 citations · 109 across the 16 of their papers we have counts for
17 papers
Planning Human-Robot Co-manipulation with Human Motor Control Objectives and Multi-component Reaching Strategies
Kevin Haninger, Luka Peternel
For successful goal-directed human-robot interaction, the robot should adapt to the intentions and actions of the collaborating human. This can be supported by musculoskeletal or d…
Combining Sampling- and Gradient-based Planning for Contact-rich Manipulation
Filippo Rozzi, Loris Roveda, Kevin Haninger
Planning over discontinuous dynamics is needed for robotics tasks like contact-rich manipulation, which presents challenges in the numerical stability and speed of planning methods…
High-speed electrical connector assembly by structured compliance in a finray-effect gripper
Richard Hartisch, Kevin Haninger
Fine assembly tasks such as electrical connector insertion have tight tolerances and sensitive components, requiring compensation of alignment errors while applying sufficient forc…
Teaching contact-rich tasks from visual demonstrations by constraint extraction
Christian Hegeler, Filippo Rozzi, Loris Roveda +1
Contact-rich manipulation involves kinematic constraints on the task motion, typically with discrete transitions between these constraints during the task. Allowing the robot to de…
Differentiable Compliant Contact Primitives for Estimation and Model Predictive Control
Kevin Haninger, Kangwagye Samuel, Filippo Rozzi +2
Control techniques like MPC can realize contact-rich manipulation which exploits dynamic information, maintaining friction limits and safety constraints. However, contact geometry…
Compliant finray-effect gripper for high-speed robotic assembly of electrical components
Richard Matthias Hartisch, Kevin Haninger
Fine assembly tasks such as electrical connector insertion have tight tolerances and sensitive components, limiting the speed and robustness of robot assembly, even when using visi…
Model Predictive Impedance Control with Gaussian Processes for Human and Environment Interaction
Kevin Haninger, Christian Hegeler, Luka Peternel
Robotic tasks which involve uncertainty--due to variation in goal, environment configuration, or confidence in task model--may require human input to instruct or adapt the robot. I…
Impedance Adaptation by Reinforcement Learning with Contact Dynamic Movement Primitives
Chunyang Chang, Kevin Haninger, Yunlei Shi +3
Dynamic movement primitives (DMPs) allow complex position trajectories to be efficiently demonstrated to a robot. In contact-rich tasks, where position trajectories alone may not b…
Flexure-based Environmental Compliance for High-speed Robotic Contact Tasks
Richard Hartisch, Kevin Haninger
The design of physical compliance -- its location, degree, and structure -- affects robot performance and robustness in contact-rich tasks. While compliance is often used in the ro…
Towards High-Payload Admittance Control for Manual Guidance with Environmental Contact
Kevin Haninger, Marcel Radke, Axel Vick +1
Force control enables hands-on teaching and physical collaboration, with the potential to improve ergonomics and flexibility of automation. Established methods for the design of co…
Model Predictive Control with Gaussian Processes for Flexible Multi-Modal Physical Human Robot Interaction
Kevin Haninger, Christian Hegeler, Luka Peternel
Physical human-robot interaction can improve human ergonomics, task efficiency, and the flexibility of automation, but often requires application-specific methods to detect human s…
Contact Information Flow and Design of Compliance
Kevin Haninger, Marcel Radke, Richard Hartisch +1
Identifying changes in contact during contact-rich manipulation can detect task state or errors, enabling improved robustness and autonomy. The ability to detect contact is affecte…
Minimum directed information: A design principle for compliant robots
Kevin Haninger
A robot's dynamics -- especially the degree and location of compliance -- can significantly affect performance and control complexity. Passive dynamics can be designed with good re…
Towards Learning Controllable Representations of Physical Systems
Kevin Haninger, Raul Vicente Garcia, Joerg Krueger
Learned representations of dynamical systems reduce dimensionality, potentially supporting downstream reinforcement learning (RL). However, no established methods predict a represe…
Safe rendering of high impedance on a series-elastic actuator with disturbance observer-based torque control
Kevin Haninger, Abner Asignacion, Sehoon Oh
An important performance metric for series-elastic actuators is the range of impedance which they can safely render. Advanced torque control, using techniques such as the disturban…
Nonparametric Inverse Dynamic Models for Multimodal Interactive Robots
Kevin Haninger, Masayoshi Tomizuka
Direct design of a robot's rendered dynamics, such as in impedance control, is now a well-established control mode in uncertain environments. When the physical interaction port var…
Bounded Collision Force by the Sobolev Norm
Kevin Haninger, Dragoljub Surdilovic
A robot making contact with an environment or human presents potential safety risks, including excessive collision force. While experiments on the effect of robot inertia, relative…