Challenges in the Locomotion of Self-Reconfigurable Modular Robots

Self-Reconfigurable Modular Robots (SRMRs) are assemblies of autonomous robotic units, referred to as modules, joined together using active connection mechanisms. By changing the connectivity of these modules, SRMRs are able to deliberately change their own shape in order to adapt to new environmental circumstances. One of the main motivations for the development of SRMRs is that conventional robots are limited in their capabilities by their morphology. The promise of the field of self-reconfigurable modular robotics is to design robots that are robust, self-healing, versatile, multi-purpose, and inexpensive. Despite significant efforts by numerous research groups worldwide, the potential advantages of SRMRs have yet to be realized. A high number of degrees of freedom and connectors make SRMRs more versatile, but also more complex both in terms of mechanical design and control algorithms. Scalability issues affect these robots in terms of hardware, low-level control, and high-level planning. In this thesis we identify and target three major challenges: (i) Hardware design; (ii) Planning and control; and, (iii) Application challenges. To tackle the hardware challenges we redesigned and manufactured the Self-Reconfigurable Modular Robot Roombots to meet desired requirements and characteristics. We explored in detail and improved two major mechanical components of an SRMR: the actuation and the connection mechanisms. We also analyzed the use of compliant extensions to increase locomotion performance in terms of locomotion speed and power consumption. We contributed to the control challenge by developing new methods that allow an arbitrary SRMR structure to learn to locomote in an efficient way. We defined a novel bio-inspired locomotion-learning framework that allows the quick and reliable optimization of new gaits after a morphological change due to self-reconfiguration or human construction. In order to find new suitable application scenarios for SRMRs we envision the use of Roombots modules to create Self-Reconfigurable Robotic Furniture. As a first step towards this vision, we explored the use and control of Plug-n-Play Robotic Elements that can augment existing pieces of furniture and create new functionalities in a household to improve quality of life

Deep Reinforcement Learning for Tensegrity Robot Locomotion

Tensegrity robots, composed of rigid rods connected by elastic cables, have a number of unique properties that make them appealing for use as planetary exploration rovers. However, control of tensegrity robots remains a difficult problem due to their unusual structures and complex dynamics. In this work, we show how locomotion gaits can be learned automatically using a novel extension of mirror descent guided policy search (MDGPS) applied to periodic locomotion movements, and we demonstrate the effectiveness of our approach on tensegrity robot locomotion. We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities. Our experiments demonstrate that our method not only learns fast, power-efficient feedback policies for rolling gaits, but that these policies can succeed with only the limited onboard sensing provided by SUPERball's accelerometers. We compare the learned feedback policies to learned open-loop policies and hand-engineered controllers, and demonstrate that the learned policy enables the first continuous, reliable locomotion gait for the real SUPERball robot. Our code and other supplementary materials are available from http://rll.berkeley.edu/drl_tensegrityComment: International Conference on Robotics and Automation (ICRA), 2017. Project website link is http://rll.berkeley.edu/drl_tensegrit

Using a Newly Developed Computer-Based Program to Evaluate Learning of Visuomotor Procedures in Children with Autism: A Pilot Study

Inspired by the recent literature, we designed a computer-based program that allows, with the aid of a digital tablet, to evaluate learning of visuomotor procedures, similar to the ones involved in handwriting. After extensive trials on children with typical development, we conducted a preliminary study to assess the effectiveness of this program in evaluating these abilities in children with ASD

On Designing an Active Tail for Legged Robots: Simplifying Control via Decoupling of Control Objectives

This work explores the possible roles of active tails for steady-state legged-locomotion. A series of simple models are proposed which capture the dynamics of an idealized running system with an active tail. The models suggest that the control objectives of injecting energy into the system and stabilizing body-pitch can be effectively decoupled via proper tail design: a long, light tail. Thus the overall control problem can be simplified, using the tail exclusively to stabilize body-pitch: this effectively relaxes the constraints on the leg-actuators, allowing them to be recruited specifically for adding energy into the system. We show in simulation that models with long-light tails are better able to reject perturbations to body-pitch than short-heavy tails with the same moment of inertia. Further, we present the results of a one degree-of-freedom tail mounted on the open-loop controlled quadruped robot Cheetah-Cub. Our results show that an active tail can greatly improve both forward velocity and reduce body-pitch per stride, while adding minimal complexity. Further, the results validate the long-light tail design: shorter, heavier tails are much more sensitive to configuration and control parameter changes than longer and lighter tails with the same moment of inertia

Robot Trotting with Segmented Legs in Simulation and Hardware.

This research is focusing on the implementation, testing, and analysis of quadrupedal, bio-inspired robot locomotion. Our tool of research is a light-weight, quadruped robot of the size of a house cat, both in simulation and hardware. We are currently following the idea of testing bio-inspired blue-prints such as leg-segmentation, directional leg compliance (bio-mechanical), and central pattern generators (bioinspired neuro-control) for their feasibility, and advantages against more traditional, engineered solutions. Clearly, our first goal would be to reach a same level of performance as animals, e.g. in terms of speed, cost of transport, or versatility. Much research has been done on bio-mechanical and neuro-physiological research on legged vertebrates. Hence, data is available for animal locomotion such as gait patterns, speed, cost of transport, duty factor, joint angles, torque patterns, body angles, and ground reaction force (GRF) data. While this data allows one to study a subset of locomotion characteristics, it often lacks an intuitive way to compare animals of different species, or as for us, quadruped robots. We started applying the collision angle analysis (Lee, Bertram, et al. 2011) for trot gait, based on qualitative and quantitative results from goats and dogs (taken from (ibid.)), and experimental recordings of our robot’s center of mass (COM) and GRF

Natural User Interface for Roombots

Roombots (RB) are self-reconfigurable modular robots designed to study robotic reconfiguration on a structured grid and adaptive locomotion off grid. One of the main goals of this platform is to create adaptive furniture inside living spaces such as homes or offices. To ease the control of RB modules in these environments, we propose a novel and more natural way of interaction with the RB modules on a RB grid, called the Natural Roombots User Interface. In our method, the user commands the RB modules using pointing gestures. The user's body is tracked using multiple Kinects. The user is also given real-time visual feedback of their physical actions and the state of the system via LED illumination electronics installed on both RB modules and the grid. We demonstrate how our interface can be used to efficiently control RB modules on simple point-to-point grid locomotion and conclude by discussing future extensions

Where to place cameras on a snake robot: Focus on camera trajectory and motion blur

Visual information is heavily used in robotics, in particular for SLAM applications. Visual SLAM algorithms depend on robust feature extraction and reliable state estimation. Quality of the visual information highly depends on how that information is captured. The nature of snake robots' locomotion presents considerable challenges on the quality of images captured by an onboard mobile camera. Although placing the camera on the "head" of the snake robot has advantages when the robot is stationary since the body can be used as a manipulator observing for the environment, how to place the camera in order to capture more useful images for navigation during locomotion is not clear. In this paper, we present a comparative study to discuss implications of the camera location on field coverage and types of image quality for three snake gaits: Rolling, sidewinding and linear progression. Camera pose during locomotion is examined in detail and quality of images are quantified using a motion blur metric which relates camera egomotion to blur. Linear progression is found to be very promising in terms of supplying sharper images. But, there are also other merits that can be exploited in different locomotion types and camera locations

Rich Locomotion Skills with the Oncilla Robot

We are motivated to better understand how adaptive locomotion (rough terrain locomotion, turning, gait transition, etc) can be realized using a quadrupedal platform with constrained resources. These constraints include computational power limitation, no accurate force/torque sensing, and partial sensing of robot’s kinematic states. These constraints arise from the fact that we are designing and experimenting with autonomous light-weight and (comparatively) cheap quadruped robots. The practical benefit of such robots is fast experimentation: experiments can be safely done with presence of one or two humans, and repairs are cheap and quick

Rich periodic motor skills on humanoid robots: Riding the pedal racer

Just as their discrete counterparts, periodic or rhythmic dynamic motion primitives allow easily modulated and robust motion generation, but for periodic tasks. In this paper we present an approach for modulating periodic dynamic movement primitives based on force feedback, allowing for rich motor behavior and skills. We propose and evaluate the combination of feedback and learned feed-forward terms to fully adapt the motions of a robot in order to achieve a desired force interaction with the environment. For the learning we employ the notion of repetitive control, which can effectively minimize the error of behavior towards a given reference. To demonstrate the approach, we show results of simulated and real world experiments on a compliant humanoid robot COMAN. We show the initial results of utilizing the approach to control a pedal-racer, a demanding balance toy best described as a hybrid between a skateboard and a bicycle. © 2014 IEEE