We present planning and control techniques for non-periodic locomotion tasks specified by temporal logic in rough cluttered terrains. Our planning approach is based on a discrete set of motion primitives for the center of mass (CoM) of a general bipedal robot model. A deterministic shortest path problem is solved over the Bu ̈chi automaton of the temporal logic task specification, composed with the graph of CoM keyframe states generated by the motion primitives. A low-level controller based on quadratic programming is proposed to track the resulting CoM and foot trajectories. We demonstrate dynamically stable, non-periodic locomotion of a kneed compass gait bipedal robot satisfying complex task specifications.
more »
« less
Resolved Motion Control for 3D Underactuated Bipedal Walking using Linear Inverted Pendulum Dynamics and Neural Adaptation
We present a framework to generate periodic trajectory references for a 3D under-actuated bipedal robot, using a linear inverted pendulum (LIP) based controller with adaptive neural regulation. We use the LIP template model to estimate the robot's center of mass (CoM) position and velocity at the end of the current step, and formulate a discrete controller that determines the next footstep location to achieve a desired walking profile. This controller is equipped on the frontal plane with a Neural-Network-based adaptive term that reduces the model mismatch between the template and physical robot that particularly affects the lateral motion. Then, the foot placement location computed for the LIP model is used to generate task space trajectories (CoM and swing foot trajectories) for the actual robot to realize stable walking. We use a fast, real-time QP-based inverse kinematics algorithm that produces joint references from the task space trajectories, which makes the formulation independent of the knowledge of the robot dynamics. Finally, we implemented and evaluated the proposed approach in simulation and hardware experiments with a Digit robot obtaining stable periodic locomotion for both cases.
more »
« less
- Award ID(s):
- 2144156
- PAR ID:
- 10404086
- Date Published:
- Journal Name:
- 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Page Range / eLocation ID:
- 6761 - 6767
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Controlling a bipedal robot that walks on a dynamic rigid surface (DRS) is a challenging task due to the complexity of the associated robot dynamics. We introduce a hybrid linear inverted pendulum (LIP) model for underactuated bipedal walking on a DRS. We also propose a discrete-time stepping controller to provably stabilize the periodic gait of the hybrid LIP.more » « less
-
Controller design for bipedal walking on dynamic rigid surfaces (DRSes), which are rigid surfaces moving in the inertial frame (e.g., ships and airplanes), remains largely underexplored. This paper introduces a hierarchical control approach that achieves stable underactuated bipedal walking on a horizontally oscillating DRS. The highest layer of our approach is a real-time motion planner that generates desired global behaviors (i.e., center of mass trajectories and footstep locations) by stabilizing a reduced-order robot model. One key novelty of this layer is the derivation of the reduced-order model by analytically extending the angular momentum based linear inverted pendulum (ALIP) model from stationary to horizontally moving surfaces. The other novelty is the development of a discrete-time foot-placement controller that exponentially stabilizes the hybrid, linear, time-varying ALIP. The middle layer translates the desired global behaviors into the robot’s full-body reference trajectories for all directly actuated degrees of freedom, while the lowest layer exponentially tracks those reference trajectories based on the full-order, hybrid, nonlinear robot model. Simulations confirm that the proposed framework ensures stable walking of a planar underactuated biped under different swaying DRS motions and gait types.more » « less
-
This work presents a hierarchical framework for bipedal locomotion that combines a Reinforcement Learning (RL)-based high-level (HL) planner policy for the online generation of task space commands with a model-based low-level (LL) controller to track the desired task space trajectories. Different from traditional end-to-end learning approaches, our HL policy takes insights from the angular momentum-based linear inverted pendulum (ALIP) to carefully design the observation and action spaces of the Markov Decision Process (MDP). This simple yet effective design creates an insightful mapping between a low-dimensional state that effectively captures the complex dynamics of bipedal locomotion and a set of task space outputs that shape the walking gait of the robot. The HL policy is agnostic to the task space LL controller, which increases the flexibility of the design and generalization of the framework to other bipedal robots. This hierarchical design results in a learning-based framework with improved performance, data efficiency, and robustness compared with the ALIP model-based approach and state-of-the-art learning-based frameworks for bipedal locomotion. The proposed hierarchical controller is tested in three different robots, Rabbit, a five-link underactuated planar biped; Walker2D, a seven-link fully-actuated planar biped; and Digit, a 3D humanoid robot with 20 actuated joints. The trained policy naturally learns human-like locomotion behaviors and is able to effectively track a wide range of walking speeds while preserving the robustness and stability of the walking gait even under adversarial conditions.more » « less
-
A control system for simulated two-dimensional bipedal walking was developed. The biped model was built based on anthropometric data. At the core of the control is a Deep Deterministic Policy Gradients (DDPG) neural network that is trained in GAZEBO, a physics simulator, to predict the ideal foot location to maintain stable walking under external impulse load. Additional controllers for hip joint movement during stance phase, and ankle joint torque during toeoff, help to stabilize the robot during walking. The simulated robot can walk at a steady pace of approximately 1m/s, and during locomotion it can maintain stability with a 30N-s impulse applied at the torso. This work implement DDPG algorithm to solve biped walking control problem. The complexity of DDPG network is decreased through carefully selected state variables and distributed control system.more » « less