227 research outputs found
Learning and Adapting Agile Locomotion Skills by Transferring Experience
Legged robots have enormous potential in their range of capabilities, from
navigating unstructured terrains to high-speed running. However, designing
robust controllers for highly agile dynamic motions remains a substantial
challenge for roboticists. Reinforcement learning (RL) offers a promising
data-driven approach for automatically training such controllers. However,
exploration in these high-dimensional, underactuated systems remains a
significant hurdle for enabling legged robots to learn performant,
naturalistic, and versatile agility skills. We propose a framework for training
complex robotic skills by transferring experience from existing controllers to
jumpstart learning new tasks. To leverage controllers we can acquire in
practice, we design this framework to be flexible in terms of their source --
that is, the controllers may have been optimized for a different objective
under different dynamics, or may require different knowledge of the
surroundings -- and thus may be highly suboptimal for the target task. We show
that our method enables learning complex agile jumping behaviors, navigating to
goal locations while walking on hind legs, and adapting to new environments. We
also demonstrate that the agile behaviors learned in this way are graceful and
safe enough to deploy in the real world.Comment: Project website: https://sites.google.com/berkeley.edu/twir
Lifelike Agility and Play on Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
Summarizing knowledge from animals and human beings inspires robotic
innovations. In this work, we propose a framework for driving legged robots act
like real animals with lifelike agility and strategy in complex environments.
Inspired by large pre-trained models witnessed with impressive performance in
language and image understanding, we introduce the power of advanced deep
generative models to produce motor control signals stimulating legged robots to
act like real animals. Unlike conventional controllers and end-to-end RL
methods that are task-specific, we propose to pre-train generative models over
animal motion datasets to preserve expressive knowledge of animal behavior. The
pre-trained model holds sufficient primitive-level knowledge yet is
environment-agnostic. It is then reused for a successive stage of learning to
align with the environments by traversing a number of challenging obstacles
that are rarely considered in previous approaches, including creeping through
narrow spaces, jumping over hurdles, freerunning over scattered blocks, etc.
Finally, a task-specific controller is trained to solve complex downstream
tasks by reusing the knowledge from previous stages. Enriching the knowledge
regarding each stage does not affect the usage of other levels of knowledge.
This flexible framework offers the possibility of continual knowledge
accumulation at different levels. We successfully apply the trained multi-level
controllers to the MAX robot, a quadrupedal robot developed in-house, to mimic
animals, traverse complex obstacles, and play in a designed challenging
multi-agent Chase Tag Game, where lifelike agility and strategy emerge on the
robots. The present research pushes the frontier of robot control with new
insights on reusing multi-level pre-trained knowledge and solving highly
complex downstream tasks in the real world
SLoMo: A General System for Legged Robot Motion Imitation from Casual Videos
We present SLoMo: a first-of-its-kind framework for transferring skilled
motions from casually captured "in the wild" video footage of humans and
animals to legged robots. SLoMo works in three stages: 1) synthesize a
physically plausible reconstructed key-point trajectory from monocular videos;
2) optimize a dynamically feasible reference trajectory for the robot offline
that includes body and foot motion, as well as contact sequences that closely
tracks the key points; 3) track the reference trajectory online using a
general-purpose model-predictive controller on robot hardware. Traditional
motion imitation for legged motor skills often requires expert animators,
collaborative demonstrations, and/or expensive motion capture equipment, all of
which limits scalability. Instead, SLoMo only relies on easy-to-obtain
monocular video footage, readily available in online repositories such as
YouTube. It converts videos into motion primitives that can be executed
reliably by real-world robots. We demonstrate our approach by transferring the
motions of cats, dogs, and humans to example robots including a quadruped (on
hardware) and a humanoid (in simulation). To the best knowledge of the authors,
this is the first attempt at a general-purpose motion transfer framework that
imitates animal and human motions on legged robots directly from casual videos
without artificial markers or labels.Comment: accepted at RA-L 2023, with ICRA 2024 optio
Agile and Versatile Robot Locomotion via Kernel-based Residual Learning
This work developed a kernel-based residual learning framework for
quadrupedal robotic locomotion. Initially, a kernel neural network is trained
with data collected from an MPC controller. Alongside a frozen kernel network,
a residual controller network is trained via reinforcement learning to acquire
generalized locomotion skills and resilience against external perturbations.
With this proposed framework, a robust quadrupedal locomotion controller is
learned with high sample efficiency and controllability, providing
omnidirectional locomotion at continuous velocities. Its versatility and
robustness are validated on unseen terrains that the expert MPC controller
fails to traverse. Furthermore, the learned kernel can produce a range of
functional locomotion behaviors and can generalize to unseen gaits
AMP in the wild: Learning robust, agile, natural legged locomotion skills
The successful transfer of a learned controller from simulation to the real
world for a legged robot requires not only the ability to identify the system,
but also accurate estimation of the robot's state. In this paper, we propose a
novel algorithm that can infer not only information about the parameters of the
dynamic system, but also estimate important information about the robot's state
from previous observations. We integrate our algorithm with Adversarial Motion
Priors and achieve a robust, agile, and natural gait in both simulation and on
a Unitree A1 quadruped robot in the real world. Empirical results demonstrate
that our proposed algorithm enables traversing challenging terrains with lower
power consumption compared to the baselines. Both qualitative and quantitative
results are presented in this paper.Comment: Video: https://youtu.be/7Ggcj6Izfh
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
Prompt a Robot to Walk with Large Language Models
Large language models (LLMs) pre-trained on vast internet-scale data have
showcased remarkable capabilities across diverse domains. Recently, there has
been escalating interest in deploying LLMs for robotics, aiming to harness the
power of foundation models in real-world settings. However, this approach faces
significant challenges, particularly in grounding these models in the physical
world and in generating dynamic robot motions. To address these issues, we
introduce a novel paradigm in which we use few-shot prompts collected from the
physical environment, enabling the LLM to autoregressively generate low-level
control commands for robots without task-specific fine-tuning. Experiments
across various robots and environments validate that our method can effectively
prompt a robot to walk. We thus illustrate how LLMs can proficiently function
as low-level feedback controllers for dynamic motion control even in
high-dimensional robotic systems. The project website and source code can be
found at: https://prompt2walk.github.io/
Learning Expressive Quadrupedal Locomotion Guided by Kinematic Trajectory Generator
Biological quadrupedal systems exhibit a wider range of locomotion skills. In Robotics, quadrupedal systems only exhibit a limited range of locomotion skills. They can be very robust for a single locomotion task, and state-of-the-art algorithms have been designed for walking gaits or use individual policies trained for a single skill. This thesis aimed to study the design of an expressive locomotion controller (different locomotion skills in one policy) for a quadrupedal robot. Different approaches based on Deep Reinforcement Learning have been studied for their recent successes in Robotics and Computer animation. A reference-free and a reference-based approach using solely reward shaping, i.e. specification of the motion through the reward, have been implemented. They produced walking gaits in simulation. Yet, the motions produced by the reference-based approach had limited footstep height and balance issues. The reference-free approach had higher footsteps and fewer base oscillations. Yet, both approaches are hard to adapt when it comes to expressiveness since the motion specification is solely done through reward shaping, which is not intuitive. Finally, inspired by works in computer animation and robotics, an approach based on motion clips for motion specification and general motion tracking has been implemented and produced more natural motions in simulation, i.e. higher footsteps, bigger strides, more base stability hard to generate using reward shaping.M.S
Words into Action: Learning Diverse Humanoid Robot Behaviors using Language Guided Iterative Motion Refinement
Humanoid robots are well suited for human habitats due to their morphological
similarity, but developing controllers for them is a challenging task that
involves multiple sub-problems, such as control, planning and perception. In
this paper, we introduce a method to simplify controller design by enabling
users to train and fine-tune robot control policies using natural language
commands. We first learn a neural network policy that generates behaviors given
a natural language command, such as "walk forward", by combining Large Language
Models (LLMs), motion retargeting, and motion imitation. Based on the
synthesized motion, we iteratively fine-tune by updating the text prompt and
querying LLMs to find the best checkpoint associated with the closest motion in
history. We validate our approach using a simulated Digit humanoid robot and
demonstrate learning of diverse motions, such as walking, hopping, and kicking,
without the burden of complex reward engineering. In addition, we show that our
iterative refinement enables us to learn 3x times faster than a naive
formulation that learns from scratch
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