139 research outputs found
Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion
Learning policies for bipedal locomotion can be difficult, as experiments are
expensive and simulation does not usually transfer well to hardware. To counter
this, we need al- gorithms that are sample efficient and inherently safe.
Bayesian Optimization is a powerful sample-efficient tool for optimizing
non-convex black-box functions. However, its performance can degrade in higher
dimensions. We develop a distance metric for bipedal locomotion that enhances
the sample-efficiency of Bayesian Optimization and use it to train a 16
dimensional neuromuscular model for planar walking. This distance metric
reflects some basic gait features of healthy walking and helps us quickly
eliminate a majority of unstable controllers. With our approach we can learn
policies for walking in less than 100 trials for a range of challenging
settings. In simulation, we show results on two different costs and on various
terrains including rough ground and ramps, sloping upwards and downwards. We
also perturb our models with unknown inertial disturbances analogous with
differences between simulation and hardware. These results are promising, as
they indicate that this method can potentially be used to learn control
policies on hardware.Comment: To appear in International Conference on Humanoid Robots (Humanoids
'2016), IEEE-RAS. (Rika Antonova and Akshara Rai contributed equally
Motion Planning and Control for the Locomotion of Humanoid Robot
This thesis aims to contribute on the motion planning and control problem of the locomotion
of humanoid robots. For the motion planning, various methods were proposed
in different levels of model dependence. First, a model free approach was proposed
which utilizes linear regression to estimate the relationship between foot placement
and moving velocity. The data-based feature makes it quite robust to handle modeling
error and external disturbance. As a generic control philosophy, it can be applied to
various robots with different gaits. To reduce the risk of collecting experimental data
of model-free method, based on the simplified linear inverted pendulum model, the
classic planning method of model predictive control was explored to optimize CoM
trajectory with predefined foot placements or optimize them two together with respect
to the ZMP constraint. Along with elaborately designed re-planning algorithm and
sparse discretization of trajectories, it is fast enough to run in real time and robust
enough to resist external disturbance. Thereafter, nonlinear models are utilized for
motion planning by performing forward simulation iteratively following the multiple
shooting method. A walking pattern is predefined to fix most of the degrees of the
robot, and only one decision variable, foot placement, is left in one motion plane and
therefore able to be solved in milliseconds which is sufficient to run in real time. In
order to track the planned trajectories and prevent the robot from falling over, diverse
control strategies were proposed according to the types of joint actuators. CoM stabilizer
was designed for the robots with position-controlled joints while quasi-static
Cartesian impedance control and optimization-based full body torque control were
implemented for the robots with torque-controlled joints. Various scenarios were set
up to demonstrate the feasibility and robustness of the proposed approaches, like
walking on uneven terrain, walking with narrow feet or straight leg, push recovery
and so on
Bipedal Walking Analysis, Control, and Applications Towards Human-Like Behavior
Realizing the essentials of bipedal walking balance is one of the core studies in both robotics and biomechanics. Although the recent developments of walking control on bipedal robots have brought the humanoid automation to a different level, the walking performance is still limited compared to human walking, which also restricts the related applications in biomechanics and rehabilitation.
To mitigate the discrepancy between robotic walking and human walking, this dissertation is broken into three parts to develop the control methods to improve three important perspectives: predictive walking behavior, gait optimization, and stepping strategy. To improve the predictive walking behavior captured by the model predictive control (MPC) which is transitionally applied with the nonlinear tracking control in sequence, a quadratic program (QP)-based controller is proposed to unify center of mass (COM) planning using MPC and a nonlinear torque control with control Lyapunov function (CLF). For the gait optimization, we focus on the algorithms of trajectory optimization with direct collocation framework. We propose a robust trajectory optimization using step-time sampling for a simple walker under terrain uncertainties. Towards generating human-like walking gait with multi-domain (phases), we improve the optimization through contact with more accurate transcription method for level walking, and generalize the hybrid zero dynamics (HZD) gait optimization with modified contact conditions for walking on various terrains. The results are compared with human walking gaits, where the similar trends and the sources of discrepancies are identified. In the third part for stepping strategy, we perform step estimation based on capture point (CP) for different human movements, including single-step (balance) recovery, walking and walking with slip. The analysis provides the insights of the efficacy and limitation of CP-based step estimation for human gait
Analytical Workspace, Kinematics, and Foot Force Based Stability of Hexapod Walking Robots
Many environments are inaccessible or hazardous for humans. Remaining debris after earthquake and fire, ship hulls, bridge installations, and oil rigs are some examples. For these environments, major effort is being placed into replacing humans with robots for manipulation purposes such as search and rescue, inspection, repair, and maintenance. Mobility, manipulability, and stability are the basic needs for a robot to traverse, maneuver, and manipulate in such irregular and highly obstructed terrain. Hexapod walking robots are as a salient solution because of their extra degrees of mobility, compared to mobile wheeled robots. However, it is essential for any multi-legged walking robot to maintain its stability over the terrain or under external stimuli. For manipulation purposes, the robot must also have a sufficient workspace to satisfy the required manipulability. Therefore, analysis of both workspace and stability becomes very important. An accurate and concise inverse kinematic solution for multi-legged robots is developed and validated. The closed-form solution of lateral and spatial reachable workspace of axially symmetric hexapod walking robots are derived and validated through simulation which aid in the design and optimization of the robot parameters and workspace. To control the stability of the robot, a novel stability margin based on the normal contact forces of the robot is developed and then modified to account for the geometrical and physical attributes of the robot. The margin and its modified version are validated by comparison with a widely known stability criterion through simulated and physical experiments. A control scheme is developed to integrate the workspace and stability of multi-legged walking robots resulting in a bio-inspired reactive control strategy which is validated experimentally
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