Tactile Feet and Online Learning for Robust Control of a Quadruped Robot in Challenging Terrain

Tactile sensing has been extensively explored in the domain of object manipulation and detection, especially with the TacTip tactile sensor, but very little has been done in the domain of tactile feet for walking robots. With feet there is a similar issue as in hands: by interacting with the environment the robot occludes the surface that needs quantifying (whether this be in texture, surface orientation, edge detection etc). Therefore the application of a tactile sensor, which directly measures the contacted surface, seems more appropriate than commonly used non-contact methods such as computer vision (CV), lidar or sonar which cannot sense the surface under contact.
The addition of tactile feet may give a walking robot more reliable estimation of slip between the feet and floor, floor distance and orientation and identification of smooth/bumpy surfaces (for selection of stable footholds), all of which improve the success of a quadruped walking and/or running over uneven natural terrain. Most walking robots struggle to do this.
Tactile feet pose additional challenges to those faced in tactile hands. The hardware must be capable of supporting the body weight and withstand the impact from walking/running and be resistant to unexpected objects encountered in natural terrain (e.g. sharp objects). The software must handle the greater distortion and noise of the sensor caused by the movements of the supported robot, which is inherently more complex and dynamic than the noise experienced in hands, and the feedback loop must be faster as walking or running require faster movements than with object grasping and manipulation. Additionally given the size and mobile nature of a walking robot, any software must either run on an onboard microcomputer, which restricts computational power, or there must be a high speed connection to an offboard computer which is fast enough to enable real time control of the robot.
Additionally, as explored in the first year project, the use of online learning methods to learn the mapping between tactile data and useful control inputs could be a data efficient and robust way of training the robot. Offline methods tend to be extremely data intensive, needing data across all possible dimensions of variation, and cannot learn to adapt to novel inputs encountered during testing. Additionally the models are learnt for specific sensors which may break and need replacing at any time,
and while replacement sensors will be similar there is enough difference that the same models cannot be used and all training must be repeated. The use of online learning solves these problems, introducing instead the challenge of creating comprehensive online data collection policies in order for the correct mappings to be learnt autonomously.
A neat extension from the first year project, which explored data-efficient online sensor training and control in edge following tasks, is the development of a beam-balancing robot. Fitting a quadruped with tactile feet would enable it to walk along a randomly curving beam only slightly wider than its leg span, without falling. With tactile sensing it is possible to identify an edge and how far along the sensor the edge is (as explored with a single sensor in the first year project). This is in contrast to a robot without tactile sensing which would be unable to tell if the foot is fully on the beam or not - the use of onboard cameras watching the feet would be obscured from seeing directly under the feet by the feet or the terrain (e.g. grass) so may be unreliable. Using tactile sensors in this way would allow the robot to move the feet back onto the beam and prevent falling (in a more complex system using maps the locations of the edge could be noted to aid future planning of stable courses).

Rapid growth in the already burgeoning Robotics and Autonomous Systems (RAS) market has been estimated from many sources. This growth is driven by socio-economic needs and enabled by advances in algorithms and technologies converging on robotics. The market potential for applications of robotics and autonomous systems is, therefore, of huge value to the UK. There are four major areas where FARSCOPE will strive to fulfil and deliver on the impact agenda.

1. Training: A coherent strategy for impact must observe the value of the 'innovation pipeline'; from training of world-class researchers to novel products in the 'shop window'. The FARSCOPE training programme described in the Case for Support will produce researchers who will be able to advance knowledge, expertise and skills in the many associated aspects of academic pursuit in the field. Crucially, they will be guided by its industrial partners and BRL's Industrial Advisory Group, so that they are grounded in the real-world context of the many robotics and autonomous systems application domains. This means pursuing research excellence while embracing the challenges set within the context of a range of real-world factors.

2. Economic and Social Exploitation: The elevated position of advanced robotics, in the commercial 'value chain', makes it imperative that we create graduates from our Centre who are acutely aware of this potential. BRL is centrally engaged in its regional SME and business ecology, as evidenced by its recent industry workshop and 'open lab' events, which attracted some 60 and 280 industrial delegates respectively. BRL is also a key contributor to regional economic innovation. BRL has engaged two business managers and allocated some dedicated space to specifically support work on RAS related industrial engagement and innovation and, importantly, technology incubation. BRL will be creating an EU-funded Robotics Innovation Facilities to help coordinating a EUR 20m a programme to specifically promote and encourage direct links between academia and industry with a focus on SMEs. All of these high-impact BRL activities will be fed directly into the FARSCOPE programme.

A critical mass of key industrial and end-user partnerships across a diverse array of sectors have given their support to the FARSCOPE centre. All have indicated their interest in engaging through the FARSCOPE mechanisms identified in the Case for Support. These demonstrate the impact of the FARSCOPE centre in engaging existing, and forming new, strategic partnerships in the RAS field.

3. Fostering links with other Research Institutions and Academic Dissemination: It is essential that FARSCOPE CDT students learn to share best practice with other RAS research centres, both in the UK and beyond. In addition to attendance and presentation at academic conferences nationally and overseas, FARSCOPE will use the following mechanisms to engage with the academic community. BRL has very many strong links with the UK, EU and global RAS research community. We will use these as a basis for cementing existing links and fostering new ones.

4. Engaging the Public: FARSCOPE will train and then encourage its student cohorts to engage with the general public, to educate about the potential of these new technologies, to participate in debates on ethics, safety and legality of autonomous systems, and to enthuse future generations to work in this exciting area. UWE and the University of Bristol, BRL's two supporting institutions, host the National Coordinating Centre for Public Engagement. In addition, UWE's Science Communication Unit is internationally renowned for its diverse and innovative activities, which engage the public with science. FARSCOPE students will receive guidance and training in public engagement in order to act as worthy RAS research 'ambassadors'.