Naveen Kuppuswamy

Do muscle synergies reduce the dimensionality of behavior

The muscle synergy hypothesis is an archetype of the notion of Dimensionality Reduction (DR) occurring in the central nervous system due to modular organization. Toward validating this hypothesis, it is important to understand if muscle synergies can reduce the state-space dimensionality while maintaining task control. In this paper we present a scheme for investigating this reduction utilizing the temporal muscle synergy formulation. Our approach is based on the observation that constraining the control input to a weighted combination of temporal muscle synergies also constrains the dynamic behavior of a system in a trajectory-specific manner. We compute this constrained reformulation of system dynamics and then use the method of system balancing for quantifying the DR; we term this approach as Trajectory Specific Dimensionality Analysis (TSDA). We then investigate the consequence of minimization of the dimensionality for a given task. These methods are tested in simulations on a linear (tethered mass) and a non-linear (compliant kinematic chain) system. Dimensionality of various reaching trajectories is compared when using idealized temporal synergies. We show that as a consequence of this Minimum Dimensional Control (MDC) model, smooth straight-line Cartesian trajectories with bell-shaped velocity profiles emerged as the optima for the reaching task. We also investigated the effect on dimensionality due to adding via-points to a trajectory. The results indicate that a trajectory and synergy basis specific DR of behavior results from muscle synergy control. The implications of these results for the synergy hypothesis, optimal motor control, motor development, and robotics are discussed.

Simultaneous state and dynamics estimation in articulated structures

Given an articulated rigid body, we define the problem of estimating its dynamics as the problem of computing all the forces and accelerations acting on the bodies which constitute the articulated system. Similarly, we define the state estimation problem as the problem of computing the system positions and velocities. In the present paper we propose a framework for simultaneous state and dynamics estimation. The estimation is framed in a Bayesian framework and a suitable Bayesian prior is defined to guarantee the physical consistency of the obtained estimation. The Bayesian posterior makes use of all available measurements which include encoders, gyroscopes, accelerometers, force and torque sensors. The proposed theoretical framework is validated both on simulation and on the iCub humanoid. The software that implements the theoretical framework is realised with an open-source license.

Harnessing the dynamics of a soft body with “timing”: Octopus inspired control via recurrent neural networks

This study aims to explore a control architecture that enables the control of a soft and flexible octopus-like arm for an object reaching task. Inspired by the division of functionality between the central and peripheral nervous systems of a real octopus, we discuss that the important factor of the control is not to regulate the arm muscles one by one but rather to control them globally with appropriate timing, and we propose an architecture equipped with a recurrent neural network (RNN). By setting the task environment for the reaching behavior, and training the network with an incremental learning strategy, we evaluate whether the network is then able to achieve the reaching behavior or not. As a result, we show that the RNN can successfully achieve the reaching behavior, exploiting the physical dynamics of the arm due to the timing based control.

Multimodal sensor fusion for foot state estimation in bipedal robots using the Extended Kalman Filter

Towards enhancing the dynamic locomotion and manipulation abilities of bipedal robots in real-world scenarios, a key problem lies in the accurate estimation of the dynamic state of the feet of the robot. In this paper, an approach is presented for estimating the dynamic pose and the internal (body) and external (ground contact) wrenches acting on the individual feet of a bipedal robot fusing haptic (compliant skin), inertial, and force/torque (F/T) measurements. Assuming rigid body dynamics on an individual foot, an Extended Kalman Filter (EKF) is used to combine ankle F/T sensor readings, contact forces computed from a compliant tactile array on the foot sole and accelerometer plus gyroscope measurements, thereby estimating both the state and the external wrenches affecting a foot through a method of state augmentation. Moreover, covariance estimation of the measurement noise was carried out for all sensors, in particular, for the skin, a bayesian-network-based regression method was chosen. The framework was implemented with the iCub humanoid robot under a toppling scenario; the estimated augmented foot state was then used to compute the Foot Rotation Indicator (FRI) trajectory as a validation through prediction of the onset of toppling and instability.

Synthesising a Motor-Primitive Inspired Control Architecture for Redundant Compliant Robots

This paper presents a control architecture for redundant and compliant robots inspired by the theory of biological motor primitives which are theorised to be the mechanism employed by the central nervous system in tackling the problem of redundancy in motor control. In our framework, inspired by self-organisational principles, the simulated robot is first perturbed by a form of spontaneous motor activity and the resulting state trajectory is utilised to reduce the control dimensionality using proper orthogonal decomposition. Motor primitives are then computed using a method based on singular value decomposition. Controllers for generating reduced dimensional commands to reach desired equilibrium positions in Cartesian space are then presented. The proposed architecture is successfully tested on a simulation of a compliant redundant robotic pendulum platform that uses antagonistically arranged series-elastic actuation.

Unsupervised Learning of a Reduced Dimensional Controller for a Tendon Driven Robot Platform

In this paper we present a developmental framework to carry out goal-oriented learning in a low-dimensional space. The framework uses two stages of learning: one to synthesise a set of motor synergies and reduce the dimensionality of the control space in an unsupervised manner, and another to carry out supervised learning in the reduced control space. We test our framework in a reaching task carried out on a (real) tendon-driven robot actuated by four artificial muscles. Our results show that the robot is capable of learning to reach using a reduced control space using no prior information about its body apart from that inherent to the unsupervised and supervised learning rules.

How to Harness the Dynamics of Soft Body: Timing Based Control of a Simulated Octopus Arm via Recurrent Neural Networks

Abstract The aim of this study is to explore a control architecture that facilitates the control of a soft and flexible octopus-like arm. Inspired by the division of functionality between the central and peripheral nervous systems of a real octopus, we note that the important requirement for control is not to regulate the arm muscles one by one but rather to control them collectively with the appropriate timing. In order to realize this timing-based control, we propose an architecture that is equipped with a recurrent neural network (RNN) and then we determine the performance of its reaching behavior. To train the network, we introduce an incremental learning strategy that is capable of taking the bodys dynamics into account. As a result, we show that the RNN can successfully accomplish the reaching behavior by exploiting the physical dynamics of the arm due to the timing-based control.

Self-calibration of joint offsets for humanoid robots using accelerometer measurements

Accurate calibration of joint offsets is a crucial requirement for effective kinematic and dynamic control of robots. Such calibration is typically carried out using cumbersome and time-consuming procedures. We propose a technique for joint offset calibration using the measurements from a distributed set of on-board accelerometers. Differently from existing techniques, we do not assume that the accelerometers are perfectly calibrated. The calibration procedure consist in a two steps automatic procedure : the first step calibrates the accelerometer measurements, while the second one computes the optimal joint offsets. The only constraint required for the kinematic chain calibration trajectory is to follow a sequence of static poses or to move under negligible robot acceleration and slow speed. The proposed approach was validated on a position control problem on the iCub humanoid robot.

Whole-Body Human Inverse Dynamics with Distributed Micro-Accelerometers, Gyros and Force Sensing

Human motion tracking is a powerful tool used in a large range of applications that require human movement analysis. Although it is a well-established technique, its main limitation is the lack of estimation of real-time kinetics information such as forces and torques during the motion capture. In this paper, we present a novel approach for a human soft wearable force tracking for the simultaneous estimation of whole-body forces along with the motion. The early stage of our framework encompasses traditional passive marker based methods, inertial and contact force sensor modalities and harnesses a probabilistic computational technique for estimating dynamic quantities, originally proposed in the domain of humanoid robot control. We present experimental analysis on subjects performing a two degrees-of-freedom bowing task, and we estimate the motion and kinetics quantities. The results demonstrate the validity of the proposed method. We discuss the possible use of this technique in the design of a novel soft wearable force tracking device and its potential applications.

Learning a Curvature Dynamic Model of an Octopus-inspired Soft Robot Arm Using Flexure Sensors☆

This work presents a novel technique for sensing body dynamics for a soft-robot arm inspired by the octopus, using flexure sensors. The aim is to develop a sensing technique which can also simultaneously enable learning of the body dynamics that can then be used for control. Flexure sensing is advantageous for a soft bodied robot since it is a direct measure of local behaviour along the arm, and is closely connected with the piecewise constant curvature assumption employed for such robots. Initial results on simulated sensor measurements and dynamics learning are presented and ongoing work and applications are discussed.