Gait generation via intrinsically stable MPC for a multi-mass humanoid model

Scianca, Nicola; Modugno, Valerio; Lanari, Leonardo; Oriolo, Giuseppe

doi:10.1109/humanoids.2017.8246926

Cited by 2 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimal trajectory is then updated real-time with sensor measurements. MPC enhances the expressivity capabilities of the motion generator and gives more compliance to the robot that can react to external unexpected contact events [26], [27]. The only drawback of using an MPC approach in a teleoperation framework is the underlying computational cost.…”

Section: Related Workmentioning

confidence: 99%

Robust Real-Time Whole-Body Motion Retargeting from Human to Humanoid

Peneo

Clément

Moduano

et al. 2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

View full text Add to dashboard Cite

Transferring the motion from a human operator to a humanoid robot is a crucial step to enable robots to learn from and replicate human movements. The ability to retarget in realtime whole-body motions that are challenging for the humanoid balance is critical to enable human to humanoid teleoperation. In this work, we design a retargeting framework that allows the robot to replicate the motion of the human operator, acquired by a wearable motion capture suit, while maintaining the whole-body balance. We introduce some dynamic filter in the retargeting to forbid dangerous motions that can make the robot fall. We validate our approach through several experiments on the iCub robot, which has a significantly different body structure and size from the one of the human operator.

show abstract

Section: Related Workmentioning

confidence: 99%

Robust Real-Time Whole-Body Motion Retargeting from Human to Humanoid

Peneo

Clément

Moduano

et al. 2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

View full text Add to dashboard Cite

show abstract

“…For compactness, we do not discuss in detail the module which provides the swing foot trajectory. It uses polynomial functions whose endpoints are adjusted in order to match the footstep positions, for example, see(Scianca et al, 2017).5 Having two different horizons for planning and control is a common occurrence, as the window over which the planner operates is generally independent of the window over which the MPC optimizes. In fact, the first can range from a few steps up to the total duration of the locomotion task, while the second is usually much shorter in order to make the problem computationally tractable online.…”

mentioning

confidence: 99%

“… 4 For compactness, we do not discuss in detail the module which provides the swing foot trajectory. It uses polynomial functions whose endpoints are adjusted in order to match the footstep positions, for example, see ( Scianca et al, 2017 ). …”

mentioning

confidence: 99%

From Walking to Running: 3D Humanoid Gait Generation via MPC

et al. 2022

Self Cite

View full text Add to dashboard Cite

We present a real time algorithm for humanoid 3D walking and/or running based on a Model Predictive Control (MPC) approach. The objective is to generate a stable gait that replicates a footstep plan as closely as possible, that is, a sequence of candidate footstep positions and orientations with associated timings. For each footstep, the plan also specifies an associated reference height for the Center of Mass (CoM) and whether the robot should reach the footstep by walking or running. The scheme makes use of the Variable-Height Inverted Pendulum (VH-IP) as a prediction model, generating in real time both a CoM trajectory and adapted footsteps. The VH-IP model relates the position of the CoM to that of the Zero Moment Point (ZMP); to avoid falling, the ZMP must be inside a properly defined support region (a 3D extension of the 2D support polygon) whenever the robot is in contact with the ground. The nonlinearity of the VH-IP is handled by splitting the gait generation into two consecutive stages, both requiring to solve a quadratic program. Thanks to a particular triangular structure of the VH-IP dynamics, the first stage deals with the vertical dynamics using the Ground Reaction Force (GRF) as a decision variable. Using the prediction given by the first stage, the horizontal dynamics become linear time-varying. During the flight phases, the VH-IP collapses to a free-falling mass model. The proposed formulation incorporates constraints in order to maintain physically meaningful values of the GRF, keep the ZMP in the support region during contact phases, and ensure that the adapted footsteps are kinematically realizable. Most importantly, a stability constraint is enforced on the time-varying horizontal dynamics to guarantee a bounded evolution of the CoM with respect to the ZMP. Furthermore, we show how to extend the technique in order to perform running on tilted surfaces. We also describe a simple technique that receives input high-level velocity commands and generates a footstep plan in the form required by the proposed MPC scheme. The algorithm is validated via dynamic simulations on the full-scale humanoid robot HRP-4, as well as experiments on the small-sized robot OP3.

show abstract

Gait generation via intrinsically stable MPC for a multi-mass humanoid model

Cited by 2 publications

References 26 publications

Robust Real-Time Whole-Body Motion Retargeting from Human to Humanoid

Robust Real-Time Whole-Body Motion Retargeting from Human to Humanoid

From Walking to Running: 3D Humanoid Gait Generation via MPC

Contact Info

Product

Resources

About