ATRIAS: Design and validation of a tether-free 3D-capable spring-mass bipedal           robot

Hubicki, Christian; Grimes, Jesse A.; Jones, Mikhail S.; Renjewski, Daniel; Spröwitz, Alexander; Abate, Andy; Hurst, Jonathan

doi:10.1177/0278364916648388

Cited by 184 publications

(113 citation statements)

References 77 publications

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“…This is a property of the system dynamics, and therefore the hardware design and not the controller design. Especially when building legged robots that try to mimic a SLIP-like behavior [27] [28], these aspects should be considered in addition to measures such as passive stability and energy efficiency.…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…This model can be fit very accurately to experimental data of many different running animals [17] [18], allows accurate prediction [19], and also has been used to design controllers for simulations [20][21] [22][23] as well as actual robots [24][25] [26]. Indeed, there has been a lot of effort to give legged robots SLIP-like behavior, either through mechanical design [27] [28] or control [24] [29]. The two dimensional view of the model, in the sagittal plane parallel to the direction of travel, represents a submanifold of the 3Dspace, and the results can be extended to 3D motion both in simulation [23] and hardware [30].…”

Section: Viability Kernel Of a Running Model A Modelmentioning

confidence: 99%

See 1 more Smart Citation

Learning from outside the viability kernel: Why we should build robots that can fall with grace

Heim¹,

Spröwitz²

2018

2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

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Despite impressive results using reinforcement learning to solve complex problems from scratch, in robotics this has still been largely limited to model-based learning with very informative reward functions. One of the major challenges is that the reward landscape often has large patches with no gradient, making it difficult to sample gradients effectively. We show here that the robot state-initialization can have a more important effect on the reward landscape than is generally expected. In particular, we show the counter-intuitive benefit of including initializations that are unviable, in other words initializing in states that are doomed to fail.

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Section: Discussion and Outlookmentioning

confidence: 99%

Section: Viability Kernel Of a Running Model A Modelmentioning

confidence: 99%

Learning from outside the viability kernel: Why we should build robots that can fall with grace

Heim¹,

Spröwitz²

2018

2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

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“…The Cassie-series robot from Agility Robotics [14] is a full 3D bipedal robot that is designed to be agile and robust. Like its predecessor ATRIAS [9], Cassie is designed with concentrated mass at its pelvis and lightweight legs with leaf springs and closed kinematic chains. The mechanical design, thus, embodies the SLIP model [2].…”

Section: Robot Modelmentioning

confidence: 99%

Bipedal Hopping: Reduced-Order Model Embedding via Optimization-Based Control

Xiong

Ames

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents the design and validation of controlling hopping on the 3D bipedal robot Cassie. A springmass model is identified from the kinematics and compliance of the robot. The spring stiffness and damping are encapsulated by the leg length, thus actuating the leg length can create and control hopping behaviors. Trajectory optimization via direct collocation is performed on the spring-mass model to plan jumping and landing motions. The leg length trajectories are utilized as desired outputs to synthesize a control Lyapunov function based quadratic program (CLF-QP). Centroidal angular momentum, taking as an addition output in the CLF-QP, is also stabilized in the jumping phase to prevent whole body rotation in the underactuated flight phase. The solution to the CLF-QP is a nonlinear feedback control law that achieves dynamic jumping behaviors on bipedal robots with compliance. The framework presented in this paper is verified experimentally on the bipedal robot Cassie.

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“…[5][6][7][8][9] Di®erent methods have been developed for mapping the terrain looking for suitable footholds by using cameras, 2 laser scanners 3 or exploratory motions. 4 Following the reactive approach, di®erent methods have been proposed by including Force-Torque (FT) sensors in the ankles or the joints [5][6][7]10,11 or even with contact switches mounted on the sole. 8,9 In this paper, a biologically-motivated approach for terrain sensing is presented by mounting robot skin [12][13][14] on the foot soles without modi¯cations on the foot architecture.…”

Section: Introductionmentioning

confidence: 99%

Plantar Tactile Feedback for Biped Balance and Locomotion on Unknown Terrain

Guadarrama-Olvera

Leon

Bergner

et al. 2020

Int. J. Human. Robot.

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This work introduces a new sensing system for biped robots based on plantar robot skin, which provides not only the resultant forces applied on the ankles but a precise shape of the pressure distribution in the sole together with other extra sensing modalities (temperature, pre-touch and acceleration). The information provided by the plantar robot skin can be used to compute the center of pressure and the ground reaction forces. This information also enables the online construction of the supporting polygon and its preemptive shape before foot landing using the proximity sensors in the robot skin. Two experiments were designed to show the advantages of this new sensing technology for improving balance and walking controllers for biped robots over unknown terrain.

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ATRIAS: Design and validation of a tether-free 3D-capable spring-mass bipedal robot

Abstract: ATRIAS is a human-scale 3D-capable bipedal robot designed to mechanically embody the

Cited by 184 publications

References 77 publications

Learning from outside the viability kernel: Why we should build robots that can fall with grace

Learning from outside the viability kernel: Why we should build robots that can fall with grace

Bipedal Hopping: Reduced-Order Model Embedding via Optimization-Based Control

Plantar Tactile Feedback for Biped Balance and Locomotion on Unknown Terrain

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