395 research outputs found
Hierarchical Experience-informed Navigation for Multi-modal Quadrupedal Rebar Grid Traversal
This study focuses on a layered, experience-based, multi-modal contact
planning framework for agile quadrupedal locomotion over a constrained rebar
environment. To this end, our hierarchical planner incorporates
locomotion-specific modules into the high-level contact sequence planner and
solves kinodynamically-aware trajectory optimization as the low-level motion
planner. Through quantitative analysis of the experience accumulation process
and experimental validation of the kinodynamic feasibility of the generated
locomotion trajectories, we demonstrate that the experience planning heuristic
offers an effective way of providing candidate footholds for a legged contact
planner. Additionally, we introduce a guiding torso path heuristic at the
global planning level to enhance the navigation success rate in the presence of
environmental obstacles. Our results indicate that the torso-path guided
experience accumulation requires significantly fewer offline trials to
successfully reach the goal compared to regular experience accumulation.
Finally, our planning framework is validated in both dynamics simulations and
real hardware implementations on a quadrupedal robot provided by Skymul Inc
Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning
We present a reinforcement learning (RL) framework that enables quadrupedal
robots to perform soccer goalkeeping tasks in the real world. Soccer
goalkeeping using quadrupeds is a challenging problem, that combines highly
dynamic locomotion with precise and fast non-prehensile object (ball)
manipulation. The robot needs to react to and intercept a potentially flying
ball using dynamic locomotion maneuvers in a very short amount of time, usually
less than one second. In this paper, we propose to address this problem using a
hierarchical model-free RL framework. The first component of the framework
contains multiple control policies for distinct locomotion skills, which can be
used to cover different regions of the goal. Each control policy enables the
robot to track random parametric end-effector trajectories while performing one
specific locomotion skill, such as jump, dive, and sidestep. These skills are
then utilized by the second part of the framework which is a high-level planner
to determine a desired skill and end-effector trajectory in order to intercept
a ball flying to different regions of the goal. We deploy the proposed
framework on a Mini Cheetah quadrupedal robot and demonstrate the effectiveness
of our framework for various agile interceptions of a fast-moving ball in the
real world.Comment: First two authors contributed equally. Accompanying video is at
https://youtu.be/iX6OgG67-Z
Motion Planning for Quadrupedal Locomotion:Coupled Planning, Terrain Mapping and Whole-Body Control
Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning method uses stochastic and derivatives-free search to plan both foothold locations and horizontal motions due to the local minima produced by the terrain model. It jointly optimizes body motion, step duration and foothold selection, and it models the terrain as a cost-map. Due to the novel attitude planning method, the horizontal motion plans can be applied to various terrain conditions. The attitude planner ensures the robot stability by imposing limits to the angular acceleration. Our whole-body controller tracks compliantly trunk motions while avoiding slippage, as well as kinematic and torque limits. Despite the use of a simplified model, which is restricted to flat terrain, our approach shows remarkable capability to deal with a wide range of noncoplanar terrains. The results are validated by experimental trials and comparative evaluations in a series of terrains of progressively increasing complexity
- …