An Equilibrium Point based Model Unifying Movement Control in Humanoids

Gu, Xiao Jun; Ballard, Dana H.

doi:10.15607/rss.2006.ii.017

Cited by 14 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an alternative to internal models, equilibrium point hypothesis (EPH) is another theoretical framework employed in human motor control field (Feldman and Latash 2005;Gu and Ballard 2006). According to the theory of EPH, movement is controlled by shifting muscle activation thresholds.…”

Section: Introductionmentioning

confidence: 99%

“…According to the theory of EPH, movement is controlled by shifting muscle activation thresholds. In other words, limb postures are interpreted as equilibrium points (Gu and Ballard 2006) and a movement is generated by transitions from one body posture to another by resetting control parameters of the motor system (Balasubramaniam and Feldman 2004). In this case, the previous position is a deviation from newly readdressed position, and the forces are generated to transfer a joint to its new equilibrium.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MODEM: a multi-agent hierarchical structure to model the human motor control system

et al. 2009

View full text Add to dashboard Cite

In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a "gate selector" block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM).

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MODEM: a multi-agent hierarchical structure to model the human motor control system

et al. 2009

View full text Add to dashboard Cite

show abstract

“…Using EPC, the motion of the robot's arm is commanded by adjusting the position of a Cartesian-space equilibrium point (CEP) that denotes where the robot's end effector would settle in the absence of externally applied forces other than gravity. Previous robotics research has looked at similar robotic control strategies in simulation [4], in free-space motions [20], legged locomotion [13], rhythmic manipulation from a fixed base [21], and in the design and control of compliant actuators [14,2].…”

Section: Related Workmentioning

confidence: 99%

Operating articulated objects based on experience

Sturm

Jain

Stachniss

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-Many tasks that would be of benefit to users in domestic environments require that robots manipulate articulated objects such as doors and drawers. In this paper, we present a novel approach that simultaneously estimates the kinematic model of an articulated object based on the trajectory described by the robot's end effector, and uses this model to predict the future trajectory of the end effector. One advantage of our approach is that the robot can directly use these predictions to generate an equilibrium point control path for operating the mechanism. Additionally, our approach can improve these predictions based on previously learned articulation models. We have implemented and tested our approach on a real mobile manipulator. Through 40 trials, we show that the robot can reliably open various household objects, including cabinet doors, sliding doors, office drawers, and a dishwasher. Furthermore, we demonstrate that using the information from previous interactions as a prior significantly improves the prediction accuracy.

show abstract

“…As such, equilibrium point control is relatively simple to use. Previous robotics research has looked at similar robotic control strategies in simulation [17], in freespace motions [18], in legged locomotion [19], in rhythmic manipulation from a fixed based [20], and in the design and control of compliant actuators [21], [22]. However, few researchers have looked at this form of control in the context of taskoriented mobile manipulation.…”

Section: Equilibrium Point Controlmentioning

confidence: 99%

Pulling open novel doors and drawers with equilibrium point control

Jain

Kemp

2009

2009 9th IEEE-RAS International Conference on Humanoid Robots

View full text Add to dashboard Cite

A large variety of doors and drawers can be found within human environments. Humans regularly operate these mechanisms without difficulty, even if they have not previously interacted with a particular door or drawer. In this paper, we empirically demonstrate that equilibrium point control can enable a humanoid robot to pull open a variety of doors and drawers without detailed prior models, and infer their kinematics in the process.Our implementation uses a 7 DoF anthropomorphic arm with series elastic actuators (SEAs) at each joint, a hook as an end effector, and low mechanical impedance. For our control scheme, each SEA applies a gravity compensating torque plus a torque from a simulated, torsional, viscoelastic spring. Each virtual spring has constant stiffness and damping, and a variable equilibrium angle. These equilibrium angles form a joint space equilibrium point (JEP), which has a corresponding Cartesian space equilibrium point (CEP) for the arm's end effector.We present two controllers that generate a CEP at each time step (ca. lOOms) and use inverse kinematics to command the arm with the corresponding JEP. One controller produces a linear CEP trajectory and the other alters its CEP trajectory based on real-time estimates of the mechanism's kinematics. We also present results from empirical evaluations of their performance (108 trials). In these trials, both controllers were robust with respect to variations in the mechanism, the pose of the base, the stiffness of the arm, and the way the handle was hooked. We also tested the more successful controller with 12 distinct mechanisms. In these tests, it was able to open 11 of the 12 mechanisms in a single trial, and successfully categorized the 11 mechanisms as having a rotary or prismatic joint, and opening to the right or left. Additionally, in the 7 out of 8 trials with rotary joints, the robot accurately estimated the location of the axis of rotation.

show abstract

An Equilibrium Point based Model Unifying Movement Control in Humanoids

Cited by 14 publications

References 20 publications

MODEM: a multi-agent hierarchical structure to model the human motor control system

MODEM: a multi-agent hierarchical structure to model the human motor control system

Operating articulated objects based on experience

Pulling open novel doors and drawers with equilibrium point control

Contact Info

Product

Resources

About