arXiv subjects

Kevin Haninger

Publications and source records attributed to Kevin Haninger.

26 records · Page 2Linked to original sources

arXiv2022

Flexure-based Environmental Compliance for High-speed Robotic Contact Tasks

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 robot's joints, flange, or end-effector, this paper proposes compliant structures in the environment, allowing safe and robust contact while keeping the higher motion control bandwidth and precision of high impedance robots. Compliance is here realized with flexures and viscoelastic materials, which are integrated to several mechanisms to offer structured compliance, such as a remote center of compliance. Additive manufacturing with fused deposition modeling is used, allowing faster design iteration and low-cost integration with standard industrial equipment. Mechanical properties, including the total stiffness matrix, stiffness ratio, and rotational precision, are analytically determined and compared to experimental results. Three remote center of compliance (RCC) devices and a 1-DOF linear device are prototyped and tested in high-speed assembly tasks.

cs.RO↗

arXiv2022

Towards High-Payload Admittance Control for Manual Guidance with Environmental Contact

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 compliance, impedance control, and \rev{collision response} can achieve free-space stability and acceptable peak contact force on lightweight, lower payload robots. Scaling collaboration to higher payloads can allow new applications, but introduces challenges due to the more significant payload dynamics and the use of higher-payload industrial robots. To achieve high-payload manual guidance with contact, this paper proposes and validates new mechatronic design methods: standard admittance control is extended with damping feedback, compliant structures are integrated to the environment, and a contact response method which allows continuous admittance control is proposed. These methods are compared with respect to free-space stability, contact stability, and peak contact force. The resulting methods are then applied to realize two contact-rich tasks on a 16 kg payload (peg in hole and slot assembly) and free-space co-manipulation of a 50 kg payload.

cs.RO↗

arXiv2021

Model Predictive Control with Gaussian Processes for Flexible Multi-Modal Physical Human Robot Interaction

Physical human-robot interaction can improve human ergonomics, task efficiency, and the flexibility of automation, but often requires application-specific methods to detect human state and determine robot response. At the same time, many potential human-robot interaction tasks involve discrete modes, such as phases of a task or multiple possible goals, where each mode has a distinct objective and human behavior. In this paper, we propose a novel method for multi-modal physical human-robot interaction that builds a Gaussian process model for human force in each mode of a collaborative task. These models are then used for Bayesian inference of the mode, and to determine robot reactions through model predictive control. This approach enables optimization of robot trajectory based on the belief of human intent, while considering robot impedance and human joint configuration, according to ergonomic- and/or task-related objectives. The proposed method reduces programming time and complexity, requiring only a low number of demonstrations (here, three per mode) and a mode-specific objective function to commission a flexible online human-robot collaboration task. We validate the method with experiments on an admittance-controlled industrial robot, performing a collaborative assembly task with two modes where assistance is provided in full six degrees of freedom. It is shown that the developed algorithm robustly re-plans to changes in intent or robot initial position, achieving online control at 15 Hz.

cs.RO↗

arXiv2021

Minimum directed information: A design principle for compliant robots

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 regions of attraction or limit cycles for a specific task, but achieving flexibility on a range of tasks requires co-design of control. This paper takes an information perspective: the robot dynamics should reduce the amount of information required for a controller to achieve a threshold of performance in a range of tasks. Towards this goal, an iterative method is proposed to minimize the directed information from state to control on discrete-time nonlinear systems. iLQG is used to find a controller and value of information, then the design parameters of the dynamics (e.g. stiffness of end-effector or joint) are optimized to reduce directed information while maintaining a minimum bound on performance. The approach is validated in simulation, on a two-mass system in contact with an uncertain wall position and a high-DOF door opening task, and shown to improve noise robustness and reduce time variance of control gains.

cs.RO↗

arXiv2020

Towards Learning Controllable Representations of Physical Systems

Learned representations of dynamical systems reduce dimensionality, potentially supporting downstream reinforcement learning (RL). However, no established methods predict a representation's suitability for control and evaluation is largely done via downstream RL performance, slowing representation design. Towards a principled evaluation of representations for control, we consider the relationship between the true state and the corresponding representations, proposing that ideally each representation corresponds to a unique true state. This motivates two metrics: temporal smoothness and high mutual information between true state/representation. These metrics are related to established representation objectives, and studied on Lagrangian systems where true state, information requirements, and statistical properties of the state can be formalized for a broad class of systems. These metrics are shown to predict reinforcement learning performance in a simulated peg-in-hole task when comparing variants of autoencoder-based representations.

cs.LG↗

arXiv2019

Safe rendering of high impedance on a series-elastic actuator with disturbance observer-based torque control

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 disturbance observer, improve torque tracking bandwidth and accuracy, but their impact on safe impedance range is not established. However, to define a safe impedance range requires a practical coupled stability condition. Here, passivity-based conditions are proposed for two variants of DOB torque control, and validated experimentally in a high-stiffness environment. While high-gain PD torque control has been shown to reduce Z-width, it is here shown that a DOB reduces the need for high-gain PD feedback and allows a higher rendered impedance. A dynamic feedforward compensator is proposed which increases the maximum safe impedance of the DOB, validated in experimentally in collision with high-stiffness environments and manual excitation.

cs.RO↗

arXiv2019

Bounded Collision Force by the Sobolev Norm

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 velocity, and interface stiffness on collision are in literature, analytical models for maximum collision force are limited to a simplified mass-spring robot model. This simplified model limits the analysis of control (force/torque, impedance, or admittance) or compliant robots (joint and end-effector compliance). Here, the Sobolev norm is adapted to be a system norm, giving rigorous bounds on the maximum force on a stiffness element in a general dynamic system, allowing the study of collision with more accurate models and feedback control. The Sobolev norm can be found through the $\mathcal{H}_2$ norm of a transformed system, allowing efficient computation, connection with existing control theory, and controller synthesis to minimize collision force. The Sobolev norm is validated, first experimentally with an admittance-controlled robot, then in simulation with a linear flexible-joint robot. It is then used to investigate the impact of control, joint flexibility and end-effector compliance on collision, and a trade-off between collision performance and environmental estimation uncertainty is shown.

cs.RO↗

arXiv2019

Nonparametric Inverse Dynamic Models for Multimodal Interactive Robots

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 variables are not measured directly, dynamic and kinematic models are required to relate the measured variables to the interaction port variables. A typical example is serial manipulators with joint torque sensors, where the interaction occurs at the end-effector. As interactive robots perform increasingly complex tasks, they will be intermittently coupled with additional dynamic elements such as tools, grippers, or workpieces, some of which should be compensated and brought to the robot side of the interaction port, making the inverse dynamics multimodal. Furthermore, there may also be unavoidable and unmeasured external input when the desired system cannot be totally isolated. Towards semi-autonomous robots, capable of handling such applications, a multimodal Gaussian process regression approach to manipulator dynamic modelling is developed. A sampling-based approach clusters different dynamic modes from unlabelled data, also allowing the seperation of perturbed data with significant, irregular external input. The passivity of the overall approach is shown analytically, and experiments examine the performance and safety of this approach on a test actuator.

cs.RO↗