A Developmental Organization for Robot Behavior

This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. In our model, the events that delineate control decisions are derived from the pattern of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential knowledge gathering and representation tasks and provide examples of the kind of developmental milestones that this approach has already produced in our lab

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments

Robotic exploration or monitoring missions require mobile robots to autonomously and safely navigate between multiple target locations in potentially challenging environments. Currently, this type of multi-goal mission often relies on humans designing a set of actions for the robot to follow in the form of a path or waypoints. In this work, we consider the multi-goal problem of visiting a set of pre-defined targets, each of which could be visited from multiple potential locations. To increase autonomy in these missions, we propose a safe multi-goal (SMUG) planner that generates an optimal motion path to visit those targets. To increase safety and efficiency, we propose a hierarchical state validity checking scheme, which leverages robot-specific traversability learned in simulation. We use LazyPRM* with an informed sampler to accelerate collision-free path generation. Our iterative dynamic programming algorithm enables the planner to generate a path visiting more than ten targets within seconds. Moreover, the proposed hierarchical state validity checking scheme reduces the planning time by 30% compared to pure volumetric collision checking and increases safety by avoiding high-risk regions. We deploy the SMUG planner on the quadruped robot ANYmal and show its capability to guide the robot in multi-goal missions fully autonomously on rough terrain

Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

A Bio-inspired Distributed Control Architecture: Coupled Artificial Signalling Networks

This thesis studies the applicability of computational models inspired by the structure and dynamics of signalling networks to the control of complex control problems. In particular, this thesis presents two different abstractions that aim to capture the signal processing abilities of biological cells: a stand-alone signalling network and a coupled signalling network. While the former mimics the interacting relationships amongst the components in a signalling pathway, the latter replicates the connectionism amongst signalling pathways. After initially investigating the feasibility of these models for controlling two complex numerical dynamical systems, Chirikov's standard map and the Lorenz system, this thesis explores their applicability to a difficult real world control problem, the generation of adaptive rhythmic locomotion patterns within a legged robotic system. The results highlight that the locomotive movements of a six-legged robot could be controlled in order to adapt the robot's trajectory in a range of challenging environments. In this sense, signalling networks are responsible for the robot adaptability and inter limb coordination as they self-adjust their dynamics according to the terrain's irregularities. More generally, the results of this thesis highlight the capacity of coupled signalling networks to decompose non-linear problems into smaller sub-tasks, which can then be independently solved

Autonomous stair climbing

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 71-73).As the face of warfare changes, the military has started to explore the application of robotics on the battlefield. Robots give soldiers a flexible, technologically advanced, disposable set of eyes and ears to assist them with their goal. This thesis deals with the design and implementation of a system to allow a small highly mobile tactical robot to climb stairs autonomously. A subsumption architecture is used to coordinate and control the maneuver. Various approaches to the problem including evolved architectures and use of contraction analysis are explored. Code was written and tested for functionality with basic test software. The functionality of parts of the system and control architecture was tested on the robot in a simulated operational environment.by Kailas Narendran.M.Eng