43 research outputs found
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.Comment: 7 pages, to appear in IROS 202
Safe Whole-Body Task Space Control for Humanoid Robots
Complex robotic systems require whole-body controllers to deal with contact
interactions, handle closed kinematic chains, and track task-space control
objectives. However, for many applications, safety-critical controllers are
important to steer away from undesired robot configurations to prevent unsafe
behaviors. A prime example is legged robotics, where we can have tasks such as
balance control, regulation of torso orientation, and, most importantly,
walking. As the coordination of multi-body systems is non-trivial, following a
combination of those tasks might lead to configurations that are deemed
dangerous, such as stepping on its support foot during walking, leaning the
torso excessively, or producing excessive centroidal momentum, resulting in
non-human-like walking. To address these challenges, we propose a formulation
of an inverse dynamics control enhanced with exponential control barrier
functions for robotic systems with numerous degrees of freedom. Our approach
utilizes a quadratic program that respects closed kinematic chains, minimizes
the control objectives, and imposes desired constraints on the Zero Moment
Point, friction cone, and torque. More importantly, it also ensures the forward
invariance of a general user-defined high Relative-Degree safety set. We
demonstrate the effectiveness of our method by applying it to the 3D biped
robot Digit, both in simulation and with hardware experiments.Comment: 8 pages, 12 figure
Real-Time Navigation for Bipedal Robots in Dynamic Environments
The popularity of mobile robots has been steadily growing, with these robots
being increasingly utilized to execute tasks previously completed by human
workers. For bipedal robots to see this same success, robust autonomous
navigation systems need to be developed that can execute in real-time and
respond to dynamic environments. These systems can be divided into three
stages: perception, planning, and control. A holistic navigation framework for
bipedal robots must successfully integrate all three components of the
autonomous navigation problem to enable robust real-world navigation. In this
paper, we present a real-time navigation framework for bipedal robots in
dynamic environments. The proposed system addresses all components of the
navigation problem: We introduce a depth-based perception system for obstacle
detection, mapping, and localization. A two-stage planner is developed to
generate collision-free trajectories robust to unknown and dynamic
environments. And execute trajectories on the Digit bipedal robot's walking
gait controller. The navigation framework is validated through a series of
simulation and hardware experiments that contain unknown environments and
dynamic obstacles.Comment: Submitted to 2023 IEEE International Conference on Robotics and
Automation (ICRA). For associated experiment recordings see
https://www.youtube.com/watch?v=WzHejHx-Kz
Safe Path Planning for Polynomial Shape Obstacles via Control Barrier Functions and Logistic Regression
Safe path planning is critical for bipedal robots to operate in
safety-critical environments. Common path planning algorithms, such as RRT or
RRT*, typically use geometric or kinematic collision check algorithms to ensure
collision-free paths toward the target position. However, such approaches may
generate non-smooth paths that do not comply with the dynamics constraints of
walking robots. It has been shown that the control barrier function (CBF) can
be integrated with RRT/RRT* to synthesize dynamically feasible collision-free
paths. Yet, existing work has been limited to simple circular or elliptical
shape obstacles due to the challenging nature of constructing appropriate
barrier functions to represent irregular-shaped obstacles. In this paper, we
present a CBF-based RRT* algorithm for bipedal robots to generate a
collision-free path through complex space with polynomial-shaped obstacles. In
particular, we used logistic regression to construct polynomial barrier
functions from a grid map of the environment to represent arbitrarily shaped
obstacles. Moreover, we developed a multi-step CBF steering controller to
ensure the efficiency of free space exploration. The proposed approach was
first validated in simulation for a differential drive model, and then
experimentally evaluated with a 3D humanoid robot, Digit, in a lab setting with
randomly placed obstacles.Comment: 7 pages, 8 figures. Supplemental Video: https://youtu.be/r_hkuK5cMw
Enhancing the performance of a safe controller via supervised learning for truck lateral control
Correct-by-construction techniques, such as control barrier functions (CBFs),
can be used to guarantee closed-loop safety by acting as a supervisor of an
existing or legacy controller. However, supervisory-control intervention
typically compromises the performance of the closed-loop system. On the other
hand, machine learning has been used to synthesize controllers that inherit
good properties from a training dataset, though safety is typically not
guaranteed due to the difficulty of analyzing the associated neural network. In
this paper, supervised learning is combined with CBFs to synthesize controllers
that enjoy good performance with provable safety. A training set is generated
by trajectory optimization that incorporates the CBF constraint for an
interesting range of initial conditions of the truck model. A control policy is
obtained via supervised learning that maps a feature representing the initial
conditions to a parameterized desired trajectory. The learning-based controller
is used as the performance controller and a CBF-based supervisory controller
guarantees safety. A case study of lane keeping for articulated trucks shows
that the controller trained by supervised learning inherits the good
performance of the training set and rarely requires intervention by the CBF
supervisorComment: submitted to IEEE Transaction of Control System Technolog
On Safety Testing, Validation, and Characterization with Scenario-Sampling: A Case Study of Legged Robots
The dynamic response of the legged robot locomotion is non-Lipschitz and can
be stochastic due to environmental uncertainties. To test, validate, and
characterize the safety performance of legged robots, existing solutions on
observed and inferred risk can be incomplete and sampling inefficient. Some
formal verification methods suffer from the model precision and other surrogate
assumptions. In this paper, we propose a scenario sampling based testing
framework that characterizes the overall safety performance of a legged robot
by specifying (i) where (in terms of a set of states) the robot is potentially
safe, and (ii) how safe the robot is within the specified set. The framework
can also help certify the commercial deployment of the legged robot in
real-world environment along with human and compare safety performance among
legged robots with different mechanical structures and dynamic properties. The
proposed framework is further deployed to evaluate a group of state-of-the-art
legged robot locomotion controllers from various model-based, deep neural
network involved, and reinforcement learning based methods in the literature.
Among a series of intended work domains of the studied legged robots (e.g.
tracking speed on sloped surface, with abrupt changes on demanded velocity, and
against adversarial push-over disturbances), we show that the method can
adequately capture the overall safety characterization and the subtle
performance insights. Many of the observed safety outcomes, to the best of our
knowledge, have never been reported by the existing work in the legged robot
literature
Rethink the Adversarial Scenario-based Safety Testing of Robots: the Comparability and Optimal Aggressiveness
This paper studies the class of scenario-based safety testing algorithms in
the black-box safety testing configuration. For algorithms sharing the same
state-action set coverage with different sampling distributions, it is commonly
believed that prioritizing the exploration of high-risk state-actions leads to
a better sampling efficiency. Our proposal disputes the above intuition by
introducing an impossibility theorem that provably shows all safety testing
algorithms of the aforementioned difference perform equally well with the same
expected sampling efficiency. Moreover, for testing algorithms covering
different sets of state-actions, the sampling efficiency criterion is no longer
applicable as different algorithms do not necessarily converge to the same
termination condition. We then propose a testing aggressiveness definition
based on the almost safe set concept along with an unbiased and efficient
algorithm that compares the aggressiveness between testing algorithms.
Empirical observations from the safety testing of bipedal locomotion
controllers and vehicle decision-making modules are also presented to support
the proposed theoretical implications and methodologies