Tony Pipe

Greg Chance, Abanoub Ghobrial, Kevin McAreavey et al.

Game engines are increasingly used as simulation platforms by the autonomous vehicle (AV) community to develop vehicle control systems and test environments. A key requirement for simulation-based development and verification is determinism, since a deterministic process will always produce the same output given the same initial conditions and event history. Thus, in a deterministic simulation environment, tests are rendered repeatable and yield simulation results that are trustworthy and straightforward to debug. However, game engines are seldom deterministic. This paper reviews and identifies the potential causes of non-deterministic behaviours in game engines. A case study using CARLA, an open-source autonomous driving simulation environment powered by Unreal Engine, is presented to highlight its inherent shortcomings in providing sufficient precision in experimental results. Different configurations and utilisations of the software and hardware are explored to determine an operational domain where the simulation precision is sufficiently low i.e.\ variance between repeated executions becomes negligible for development and testing work. Finally, a method of a general nature is proposed, that can be used to find the domains of permissible variance in game engine simulations for any given system configuration.

T. Pardi, V. Ortenzi, C. Fairbairn et al.

This paper presents a method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points. The robot has no prior knowledge of the surface shape, but observes it from a noisy point-cloud camera. We consider the multi-objective optimisation problem of finding robot trajectories which maximise the robot's manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of \textit{rough} robotic cutting, \textit{e.g.} demolition of legacy nuclear plant, where the cut path need not be precise as long as it achieves dismantling. We show how detours in the cut path can be leveraged, to increase the manipulability of the robot at all points along the path. This helps avoid singularities, while maximising the robot's capability to make small deviations during task execution, \textit{e.g.} compliantly responding to cutting forces via impedance control. We show how a sampling-based planner can be projected onto the Riemannian manifold of a curved surface, and extended to include a term which maximises manipulability. We present the results of empirical experiments, with both simulated and real robots, which are tasked with moving over a variety of different surface shapes. Our planner enables successful task completion, while avoiding singularities and ensuring significantly greater manipulability when compared against a conventional RRT* planner.

Dejanira Araiza-Illan, Anthony G. Pipe, Kerstin Eder

Robotic code needs to be verified to ensure its safety and functional correctness, especially when the robot is interacting with people. Testing real code in simulation is a viable option. However, generating tests that cover rare scenarios, as well as exercising most of the code, is a challenge amplified by the complexity of the interactions between the environment and the software. Model-based test generation methods can automate otherwise manual processes and facilitate reaching rare scenarios during testing. In this paper, we compare using Belief-Desire-Intention (BDI) agents as models for test generation with more conventional automata-based techniques that exploit model checking, in terms of practicality, performance, transferability to different scenarios, and exploration (`coverage'), through two case studies: a cooperative manufacturing task, and a home care scenario. The results highlight the advantages of using BDI agents for test generation. BDI agents naturally emulate the agency present in Human-Robot Interactions (HRIs), and are thus more expressive than automata. The performance of the BDI-based test generation is at least as high, and the achieved coverage is higher or equivalent, compared to test generation based on model checking automata.

Adriana Hamacher, Nadia Bianchi-Berthouze, Anthony G. Pipe et al.

Strategies are necessary to mitigate the impact of unexpected behavior in collaborative robotics, and research to develop solutions is lacking. Our aim here was to explore the benefits of an affective interaction, as opposed to a more efficient, less error prone but non-communicative one. The experiment took the form of an omelet-making task, with a wide range of participants interacting directly with BERT2, a humanoid robot assistant. Having significant implications for design, results suggest that efficiency is not the most important aspect of performance for users; a personable, expressive robot was found to be preferable over a more efficient one, despite a considerable trade off in time taken to perform the task. Our findings also suggest that a robot exhibiting human-like characteristics may make users reluctant to 'hurt its feelings'; they may even lie in order to avoid this.

Dejanira Araiza-Illan, Tony Pipe, Kerstin Eder

The software of robotic assistants needs to be verified, to ensure its safety and functional correctness. Testing in simulation allows a high degree of realism in the verification. However, generating tests that cover both interesting foreseen and unforeseen scenarios in human-robot interaction (HRI) tasks, while executing most of the code, remains a challenge. We propose the use of belief-desire-intention (BDI) agents in the test environment, to increase the level of realism and human-like stimulation of simulated robots. Artificial intelligence, such as agent theory, can be exploited for more intelligent test generation. An automated testbench was implemented for a simulation in Robot Operating System (ROS) and Gazebo, of a cooperative table assembly task between a humanoid robot and a person. Requirements were verified for this task, and some unexpected design issues were discovered, leading to possible code improvements. Our results highlight the practicality of BDI agents to automatically generate valid and human-like tests to get high code coverage, compared to hand-written directed tests, pseudorandom generation, and other variants of model-based test generation. Also, BDI agents allow the coverage of combined behaviours of the HRI system with more ease than writing temporal logic properties for model checking.

Dejanira Araiza-Illan, David Western, Anthony Pipe et al.

Collaborative robots could transform several industries, such as manufacturing and healthcare, but they present a significant challenge to verification. The complex nature of their working environment necessitates testing in realistic detail under a broad range of circumstances. We propose the use of Coverage-Driven Verification (CDV) to meet this challenge. By automating the simulation-based testing process as far as possible, CDV provides an efficient route to coverage closure. We discuss the need, practical considerations, and potential benefits of transferring this approach from microelectronic design verification to the field of human-robot interaction. We demonstrate the validity and feasibility of the proposed approach by constructing a custom CDV testbench and applying it to the verification of an object handover task.