Nonlinear PD plus sliding mode control with application to a parallel delta robot

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This paper builds up the skill of impact aware non prehensile manipulation through a hitting motion by allowing the robot arm to come in contact with the environment with parts other than its end effector. Hitting with other joints allows us to manipulate ...

One can estimate the velocity and acceleration of robot manipulators by utilizing nonlinear observers. This involves combining inertial measurement units (IMUs) with the motor encoders of the robot through a model-based sensor fusion technique. This approa ...

Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics ...

In this paper, we propose a metric called hitting flux which is used in the motion generation and controls for a robot manipulator to interact with the environment through a hitting or a striking motion. Given the task of placing a known object outside of ...

In construction robotics, a conventional design-to-fabrication work-flow starts with designing a structure, followed by task and robotic motion planning, and ultimately, fabrication. However, this approach can prove unsuccessful, as we may only discover th ...

The digitization of timber construction, the emergence of engineered wood products, and the urgent need to drastically reduce buildings' environmental impact have given a rebirth to wood as a construction material. On the one hand, increasing the use of ti ...

Programming intelligent robots requires robust controllers that can achieve desired tasks while adapting to the changes in the task and the environment. In this thesis, we address the challenges in designing such adaptive and anticipatory feedback controll ...

Humans coordinate the abundant degrees of freedom (DoFs) of hands to dexterously perform tasks in everyday life. We imitate human strategies to advance the dexterity of multi-DoF robotic hands. Specifically, we enable a robot hand to grasp multiple objects ...

Humans can express their actions and intentions, resorting to verbal and/or non-verbal communication. In verbal communication, humans use language to express, in structured linguistic terms, the desired action they wish to perform. Non-verbal communication ...

The thesis at hand is concerned with robots' navigation in human crowds. Specifically, methods are developed for planning a mobile robot's local motion between pedestrians, and they are evaluated in experiments where a robot interacts with real pedestrians ...

Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but requir ...

Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector. No generic and autonomous method does exist to generate these movements without making hypotheses on the robot or on the object. Lea ...

In this paper, we present an approach for learning a neural implicit signed distance function expressed in joint space coordinates, that efficiently computes distance-to-collisions for arbitrary robotic manipulator configurations. Computing such distances ...

Picking up objects to toss them on a conveyor belt are activities generated on a daily basis in the industry. These tasks are still done largely by humans. This paper proposes a unified motion generator for a bimanual robotic systems that enables two 7 deg ...

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

Recently developed Concentric Tube Continuum Robots (CTCRs) are widely exploited in, for example in minimally invasive surgeries which involve navigating inside narrow body cavities close to sensitive regions. These CTCRs can be controlled by extending and ...

Automatic control design for robotic systems is becoming more and more popular. However, this usually involves a significant computational cost, due to the expensive and noisy evaluation of candidate solutions through highfidelity simulation or even real h ...

Humans have an incredible sense of self-preservation that is both instilled, and also learned through experience. One system which contributes to this is the pain and reflex system which both minimizes damage through involuntary reflex ac- tions and also s ...

Collision resilience is an important feature of robots deployed in unstructured and partially unpredictable environments. Herein, a novel dual stiffness (DS) tensegrity platform to integrate collision resilience into a robot body is proposed. The proposed ...

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation. However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning per ...

Nonlinear PD plus sliding mode control with application to a parallel delta robot

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Hitting with Different Joints of a Robotic Manipulator

A Stable Adaptive Extended Kalman Filter for Estimating Robot Manipulators Link Velocity and Acceleration

Exploration-based model learning with self-attention for risk-sensitive robot control

Motion Planning and Inertia Based Control for Impact Aware Manipulation

Reinforcement learning for scaffold-free construction of spanning-structures

Robotic Assembly of Integrally-Attached Timber Plate Structures: From Computational Design to Automated Construction

Learning and optimization of anticipatory feedback controllers for robot manipulation

Exploiting Kinematic Redundancy for Robotic Grasping of Multiple Objects

Non-verbal Communication between Humans and Robots: Imitation, Mutual Understanding and Inferring Object Properties

Geometric and Learning Methods for Robots to Navigate in Human Crowds with Application to Smart Mobility Devices

Training Efficient Controllers via Analytic Policy Gradient

Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search

Neural Joint Space Implicit Signed Distance Functions for Reactive Robot Manipulator Control

Dual-Arm Control for Coordinated Fast Grabbing and Tossing of an Object

Memory of Motion for Initializing Optimization in Robotics

Navigation of Concentric Tube Continuum Robots using Optimal Control

Leveraging Multi-Level Modelling to Automatically Design Behavioral Arbitrators in Robotic Controllers

Bio-inspired Reflex System for Learning Visual Information for Resilient Robotic Manipulation

Dual Stiffness Tensegrity Platform for Resilient Robotics

Efficient Configuration Exploration in Inverse Dynamics Acquisition of Robotic Manipulators