We were pleased to see Elio Jabbour defend his PhD thesis on “Shared-autonomy control for improving Human-Robot collaboration in haptic teleoperation”.

Abstract

Shared control frameworks assist human operators by blending their commands with autonomous, goal-oriented trajectories. However, conventional blending techniques often fail to guarantee the feasibility of the resulting motion or the optimality of the combined decision. This thesis addresses two principal gaps in shared control: 1) the lack of a blending arbitrator that unifies predictive foresight with verifiable safety in a computationally tractable manner, and 2) the flawed assumption that the autonomous assistance is correct, which leads to performance degradation and user-robot conflict when the system’s world model is misaligned with reality.

We were pleased to see Alexis Boulay defend his PhD thesis on “Assisting humans through adaptable haptic guidance, application to remote-controlled vertical farming”.

Abstract

In vertical farming, teleoperation enables human skills to be leveraged for plant manipulation while maintaining a pest and disease free growing environment. A key component of teleoperation is the exchange of information between the operator and the robotic system such as visual feedback (video streams) and haptic feedback (physical interaction cues with the environment). This thesis focuses on the use of haptic feedback through haptic guidance.

We were pleased to see Benjamin Camblor defend his PhD thesis on “Exploiting robot motion to improve situation awareness in human-robot collaboration”.

Abstract

One of the challenges of Industry 4.0 is to preserve the health and comfort of operators while improving their productivity. Collaborative robotics is a solution which, through appropriate assistance, enables the operator to focus on tasks for which he has expertise, while delegating loads and constraints to a collaborative robot. This involves combining the strengths of industrial robots (high physical capacity, repeatability, strength, endurance, speed, etc.) with those of humans (variability, reactions to uncertainty). An analysis of industrial robotics accidents highlighted a number of similar accident patterns linked to poor situational awareness. In addition, it seems that the lack of situation awareness is due to poor use of the means of communication. This thesis proposes to use robot motion as a means of communication that supports human situation awareness in human-robot collaboration. One of the important points of our contribution is that these movements, referred to as signaling motions, can be generated while enabling the robot to perform actions thanks to the redundancy of its joints.

Abstract

This thesis is based on vision of the future where robotics and industry are centred around humans, emphasising collaboration between humans and robots rather than mere automation.

In this collaborative future, robots serve as active assistants, coexisting closely with humans and engaging in physical interactions to execute tasks. Such symbiotic systems leverage the unique abilities of both humans and robots, enhancing efficiency and prioritising human safety and well-being through personalised robotic assistance.

Abstract

Collaborative robotics involves the transformation of industrial robots to function in shared workspaces alongside humans, resulting in more compact and manageable robotic systems. This evolution necessitates a reevaluation of the requirements for robot controllers. Safety remains paramount, but robots must also adapt to dynamic contexts where objects are in constant motion, and conditions are ever-changing as people carry out their tasks.

To address these challenges, this work proposes using Model Predictive Control (MPC) to empower the robot to dynamically adjust its operations in a responsive manner. This enables the implementation of human-aware behaviors that prioritize safety, as detailed in [1]. The methodology, based on linear MPC, primarily focuses on precise position and orientation control, leveraging a mathematical formalism outlined in a conference paper [2].This control architecture serves as a versatile framework capable of accommodating complex tasks that extend beyond simple point-to-point movements in space, as discussed in [3]. Through real-time, high-frequency replanning, this work showcases the robot’s agility in responding to evolving tasks and dynamic environments.