Simulation-based internal models for safer robots

Readers of this blog will know that I've become very excited by the potential of robots with simulation-based internal models in recent years. So far we've demonstrated their potential in simple ethical robots and as the basis for rational imitation. Our most recent publication instead examines the potential of robots with simulation-based internal models for safety. Of course it's not hard to see why the ability to model and predict the consequences of both your own and others' actions can help you to navigate the world more safely than without that ability.

Our paper Simulation-Based Internal Models for Safer Robots demonstrates the value of anticipation in what we call the corridor experiment. Here a smart robot (equipped with a simulation based internal model which we call a consequence engine) must navigate to the end of a corridor while maintaining a safe space around it at all times despite five other robots moving randomly in the corridor - in much the same way you and I might have to navigate down a busy office corridor while others are coming in the opposite direction.

Here is the abstract from our paper:

In this paper, we explore the potential of mobile robots with simulation-based internal models for safety in highly dynamic environments. We propose a robot with a simulation of itself, other dynamic actors and its environment, inside itself. Operating in real time, this simulation-based internal model is able to look ahead and predict the consequences of both the robot’s own actions and those of the other dynamic actors in its vicinity. Hence, the robot continuously modifies its own actions in order to actively maintain its own safety while also achieving its goal. Inspired by the problem of how mobile robots could move quickly and safely through crowds of moving humans, we present experimental results which compare the performance of our internal simulation-based controller with a purely reactive approach as a proof-of-concept study for the practical use of simulation-based internal models.

So, does it work? Thanks to some brilliant experimental work by Christian Blum the answer is a resounding yes. The best way to understand what's going on is with this wonderful gif animation of one experimental run below. The smart robot (blue) starts at the left and has the goal of safely reaching the right hand end of the corridor – its actual path is also shown in blue. Meanwhile 5 (red) robots are moving randomly (including bouncing off walls) and their actual paths are also shown in red; these robots are equipped only with simple obstacle avoidance behaviours. The larger blue circle shows blue's 'attention radius' – to reduce computational effort blue will only model red robots within this radius. The yellow paths in front of the red robots in blue's attention radius show blue's predictions of how those robots will move (taking into account collisions with the corridor walls and with blue and each other). The light blue projection in front of blue shows which of the 34 next possible actions of blue that is internally modelled is actually chosen as the next action (which, as you will see, sometimes includes standing still).

What do the results show us? Christian ran lots of trials – 88 simulations and 54 real robot experiments – over four experiments: (1) the baseline in simulation – in which the blue robot has only a simple reactive collision avoidance behaviour, (2) the baseline with real robots, (3) using the consequence engine (CE) in the blue robot in simulation, and (4) using the consequence engine in the blue robot with real robots. In the results below (a) shows the time taken for the blue robot to reach the end of the corridor, (b) shows the distance that the blue robot covers while reaching the end of the corridor, (c) shows the “danger ratio” experienced by the blue robot, and (d) shows the number of consequence engine runs per timestep in the blue robot. The danger ratio is the percentage of the run time that anther robot is within the blue robot’s safety radius.

For a relatively small cost in additional run time and distance covered, panels (a) and (b), the danger ratio is very significantly reduced from a mean value of ~20% to a mean value of zero, panel (c). Of course there is a computational cost, and this is reflected in panel (d); the baseline experiment has no consequence engine and hence runs no simulations, whereas the smart robot runs an average of between 8 and 10 simulations per time-step. This is exactly what we would expect: predicting the future clearly incurs a computational overhead.

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Full paper reference:

Blum C, Winfield AFT and Hafner VV (2018) Simulation-Based Internal Models for Safer Robots. Front. Robot. AI 4:74. doi: 10.3389/frobt.2017.00074

Acknowledgements:
I am indebted to Christian Blum who programmed the robots, set up the experiment and obtained the results outlined here. Christian lead authored the paper, which was also co-authored by my friend and research collaborator Verena Hafner, who was Christian's PhD advisor.

Surgical micro-robot swarms: science fiction, or realistic prospect?

Imagine a swarm of microscopic robots that we inject into the vascular system: the swarm swims to the source of the problem, then either delivers therapeutics or undertakes microsurgery directly.

That was how I opened a short invited talk at the Royal Society of Medicine on 5 February, at a meeting themed The Future of Robotics in Surgery. The talk was a wonderful opportunity for me to introduce swarm intelligence and speculate on the likelihood of surgical micro-robot swarms, while at the same time learning about robot surgery. Here are the slides from my talk (with links to YouTube videos where available).



The talk was in three parts.

First I introduced swarm intelligence, and its artificial counterpart swarm robotics. I showed, with examples from two of my students, how - with very simple rules - a swarm of robots can keep together as a swarm, while moving toward a beacon. Then, with a phagocyte-like behaviour, encapsulate the beacon. In our case these were lab robots moving toward an infra-red beacon, but it's not hard to imagine the same behavioural rules in a microscopic swarm swimming toward the source of a chemical marker (chemotaxis). I then gave two examples of the state of the art in swarm robotics: SYMBRION and (my current favourite) TERMES. I wanted to illustrate emergent physical interaction, in these two cases swarm self-assembly and swarm construction, respectively.

In part two I outlined what is by far the biggest problem: actually engineering robots at the micro-scale. Here I drew upon the examples from my book Robotics: a very short introduction; a section called A swarm of medical microrobots.  Start with cm sized robots. These already exist in the form of pillbots and I reference the work of Paolo Dario's lab in this direction. Then get 10 times smaller to mm sized robots. Here we're at the limit of making robots with conventional mechatronics. The almost successful I-SWARM project prototyped remarkable robots measuring 4 x 4 x 3mm. But now shrink by another 3 orders of magnitude to microbots, measured in micrometers. This is how small robots would have to be in order to swim through and access (most of) the vascular system. Here we are far beyond conventional materials and electronics, but amazingly work is going on to control bacteria. In the example I give from the lab of Sylvain Martel, swarms of magnetotactic bacteria are steered by an external magnetic field and, interestingly, tracked in an MRI scanner.

In the final part of my talk I introduce the work of my colleague Sabine Hauert, on swarms of nanoparticles for cancer nanomedicine. These 5 - 500nm particles are controlled by changing their body size, material, coating and cargo so - in true swarm fashion - the way the nanoparticle swarm moves and interacts with much larger normal and tumour cells is an emergent property of the way the nanoparticles individually interact and cooperate. Sabine and her collaborators have created an online tool called NanoDoc, which allows anyone to edit the design of nanoparticles then run simulations to see how their designs perform. In this way the task of searching the huge design space is crowd-sourced. In parallel Sabine is also running mesoscale embodied simulations, using the Harvard Kilobots.

I concluded by suggesting that engineering micro or nanobots is not the only major challenge. At least as important are: (a) how would you program the swarm, and (b) how would such a swarm be approved for clinical use? But a deeply interesting question is the nature of the human-swarm interface. If a swarm of surgical microbots should become a practical proposition would we treat the swarm as a microscopic instrument under the surgeon’s control, or a smart drug that does surgery?

Robots for Risky Interventions

Returning on the Eurostar from a really interesting workshop in Brussels, on Robots for Risky Interventions and Environmental Surveillance (RISE 09). The focus of the workshop was a number of EU funded projects aimed at developing multi-robot systems in safety-critical applications. One project called GUARDIANS, led by Jacques Penders at Sheffield-Hallam, is aimed at providing firefighters with robot outriders, providing sensing and navigation that - in effect - give the firefighter extended super-senses. I learned that one of the most dangerous situations they have to deal with is large warehouse fires which quickly fill with smoke, making it very easy for firefighters to become lost and disoriented in the labyrinth of aisles between storage racks. But the flat smooth warehouse floor and grid like layout is of course ideal for mobile robots, making this a really good application for robots to prove themselves useful in a serious and worthwhile real-world task.

I gave a talk setting out the potential of using a swarm robotics approach to safety-critical applications. The swarm approach differs from the conventional multi-robot systems approach in its control paradigm. A multi-robot system will typically use a centralised command and control system to both direct the actions of individual robots and coordinate the whole group. In contrast a swarm uses a completely decentralised, distributed approach, in which each robot decides how to act autonomously - using local sensing and communication with neighbouring robots - so that the swarm self-organises to achieve the overall task or mission. Although the robots may look the same in both cases, the swarm approach is radically different from a systems control point of view. But the swarm approach offers the potential of much higher resilience to failure (of individual robots, for instance).