488 research outputs found
Continuous Versatile Jumping Using Learned Action Residuals
Jumping is essential for legged robots to traverse through difficult
terrains. In this work, we propose a hierarchical framework that combines
optimal control and reinforcement learning to learn continuous jumping motions
for quadrupedal robots. The core of our framework is a stance controller, which
combines a manually designed acceleration controller with a learned residual
policy. As the acceleration controller warm starts policy for efficient
training, the trained policy overcomes the limitation of the acceleration
controller and improves the jumping stability. In addition, a low-level
whole-body controller converts the body pose command from the stance controller
to motor commands. After training in simulation, our framework can be deployed
directly to the real robot, and perform versatile, continuous jumping motions,
including omni-directional jumps at up to 50cm high, 60cm forward, and
jump-turning at up to 90 degrees. Please visit our website for more results:
https://sites.google.com/view/learning-to-jump.Comment: To be presented at L4DC 202
SayTap: Language to Quadrupedal Locomotion
Large language models (LLMs) have demonstrated the potential to perform
high-level planning. Yet, it remains a challenge for LLMs to comprehend
low-level commands, such as joint angle targets or motor torques. This paper
proposes an approach to use foot contact patterns as an interface that bridges
human commands in natural language and a locomotion controller that outputs
these low-level commands. This results in an interactive system for quadrupedal
robots that allows the users to craft diverse locomotion behaviors flexibly. We
contribute an LLM prompt design, a reward function, and a method to expose the
controller to the feasible distribution of contact patterns. The results are a
controller capable of achieving diverse locomotion patterns that can be
transferred to real robot hardware. Compared with other design choices, the
proposed approach enjoys more than 50% success rate in predicting the correct
contact patterns and can solve 10 more tasks out of a total of 30 tasks. Our
project site is: https://saytap.github.io
RSG: Fast Learning Adaptive Skills for Quadruped Robots by Skill Graph
Developing robotic intelligent systems that can adapt quickly to unseen wild
situations is one of the critical challenges in pursuing autonomous robotics.
Although some impressive progress has been made in walking stability and skill
learning in the field of legged robots, their ability to fast adaptation is
still inferior to that of animals in nature. Animals are born with massive
skills needed to survive, and can quickly acquire new ones, by composing
fundamental skills with limited experience. Inspired by this, we propose a
novel framework, named Robot Skill Graph (RSG) for organizing massive
fundamental skills of robots and dexterously reusing them for fast adaptation.
Bearing a structure similar to the Knowledge Graph (KG), RSG is composed of
massive dynamic behavioral skills instead of static knowledge in KG and enables
discovering implicit relations that exist in be-tween of learning context and
acquired skills of robots, serving as a starting point for understanding subtle
patterns existing in robots' skill learning. Extensive experimental results
demonstrate that RSG can provide rational skill inference upon new tasks and
environments and enable quadruped robots to adapt to new scenarios and learn
new skills rapidly
A Novel Lockable Spring-loaded Prismatic Spine to Support Agile Quadrupedal Locomotion
This paper introduces a way to systematically investigate the effect of
compliant prismatic spines in quadrupedal robot locomotion. We develop a novel
spring-loaded lockable spine module, together with a new Spinal
Compliance-Integrated Quadruped (SCIQ) platform for both empirical and
numerical research. Individual spine tests reveal beneficial spinal
characteristics like a degressive spring, and validate the efficacy of a
proposed compact locking/unlocking mechanism for the spine. Benchmark vertical
jumping and landing tests with our robot show comparable jumping performance
between the rigid and compliant spines. An observed advantage of the compliant
spine module is that it can alleviate more challenging landing conditions by
absorbing impact energy and dissipating the remainder via feet slipping through
much in cat-like stretching fashion.Comment: To appear in 2023 IEEE IRO
The Flying Monkey: a Mesoscale Robot that can Run, Fly, and Grasp
The agility and ease of control make a quadrotor aircraft an attractive platform for studying swarm behavior, modeling, and control. The energetics of sustained flight for small aircraft, however, limit typical applications to only a few minutes. Adding payloads – and the mechanisms used to manipulate them – reduces this flight time even further. In this paper we present the flying monkey, a novel robot platform having three main capabilities: walking, grasping, and flight. This new robotic platform merges one of the world’s smallest quadrotor aircraft with a lightweight, single-degree-of-freedom walking mechanism and an SMA-actuated gripper to enable all three functions in a 30 g package. The main goal and key contribution of this paper is to design and prototype the flying monkey that has increased mission life and capabilities through the combination of the functionalities of legged and aerial roots.National Science Foundation (U.S.) (IIS-1138847)National Science Foundation (U.S.) (EFRI-124038)National Science Foundation (U.S.) (CCF-1138967)United States. Army Research Laboratory (W911NF-08-2-0004)Wyss Institute for Biologically Inspired Engineerin
HyperDog: An Open-Source Quadruped Robot Platform Based on ROS2 and micro-ROS
Nowadays, design and development of legged quadruped robots is a quite active
area of scientific research. In fact, the legged robots have become popular due
to their capabilities to adapt to harsh terrains and diverse environmental
conditions in comparison to other mobile robots. With the higher demand for
legged robot experiments, more researches and engineers need an affordable and
quick way of locomotion algorithm development. In this paper, we present a new
open source quadruped robot HyperDog platform, which features 12 RC servo
motors, onboard NVIDIA Jetson nano computer and STM32F4 Discovery board.
HyperDog is an open-source platform for quadruped robotic software development,
which is based on Robot Operating System 2 (ROS2) and micro-ROS. Moreover, the
HyperDog is a quadrupedal robotic dog entirely built from 3D printed parts and
carbon fiber, which allows the robot to have light weight and good strength.
The idea of this work is to demonstrate an affordable and customizable way of
robot development and provide researches and engineers with the legged robot
platform, where different algorithms can be tested and validated in simulation
and real environment. The developed project with code is available on GitHub
(https://github.com/NDHANA94/hyperdog_ros2).Comment: 6 pages, 13 figures, IEEE SMC 2022 conferenc
The implications of embodiment for behavior and cognition: animal and robotic case studies
In this paper, we will argue that if we want to understand the function of
the brain (or the control in the case of robots), we must understand how the
brain is embedded into the physical system, and how the organism interacts with
the real world. While embodiment has often been used in its trivial meaning,
i.e. 'intelligence requires a body', the concept has deeper and more important
implications, concerned with the relation between physical and information
(neural, control) processes. A number of case studies are presented to
illustrate the concept. These involve animals and robots and are concentrated
around locomotion, grasping, and visual perception. A theoretical scheme that
can be used to embed the diverse case studies will be presented. Finally, we
will establish a link between the low-level sensory-motor processes and
cognition. We will present an embodied view on categorization, and propose the
concepts of 'body schema' and 'forward models' as a natural extension of the
embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of
Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5
Learning Agile Bipedal Motions on a Quadrupedal Robot
Can a quadrupedal robot perform bipedal motions like humans? Although
developing human-like behaviors is more often studied on costly bipedal robot
platforms, we present a solution over a lightweight quadrupedal robot that
unlocks the agility of the quadruped in an upright standing pose and is capable
of a variety of human-like motions. Our framework is with a bi-level structure.
At the low level is a motion-conditioned control policy that allows the
quadrupedal robot to track desired base and front limb movements while
balancing on two hind feet. The policy is commanded by a high-level motion
generator that gives trajectories of parameterized human-like motions to the
robot from multiple modalities of human input. We for the first time
demonstrate various bipedal motions on a quadrupedal robot, and showcase
interesting human-robot interaction modes including mimicking human videos,
following natural language instructions, and physical interaction
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