3 research outputs found
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples
Learning controllers that reproduce legged locomotion in nature has been a
long-time goal in robotics and computer graphics. While yielding promising
results, recent approaches are not yet flexible enough to be applicable to
legged systems of different morphologies. This is partly because they often
rely on precise motion capture references or elaborate learning environments
that ensure the naturality of the emergent locomotion gaits but prevent
generalization. This work proposes a generic approach for ensuring realism in
locomotion by guiding the learning process with the spring-loaded inverted
pendulum model as a reference. Leveraging on the exploration capacities of
Reinforcement Learning (RL), we learn a control policy that fills in the
information gap between the template model and full-body dynamics required to
maintain stable and periodic locomotion. The proposed approach can be applied
to robots of different sizes and morphologies and adapted to any RL technique
and control architecture. We present experimental results showing that even in
a model-free setup and with a simple reactive control architecture, the learned
policies can generate realistic and energy-efficient locomotion gaits for a
bipedal and a quadrupedal robot. And most importantly, this is achieved without
using motion capture, strong constraints in the dynamics or kinematics of the
robot, nor prescribing limb coordination. We provide supplemental videos for
qualitative analysis of the naturality of the learned gaits.Comment: Accepted to ICRA 202
On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis
We present a comprehensive study on discrete morphological symmetries of
dynamical systems, which are commonly observed in biological and artificial
locomoting systems, such as legged, swimming, and flying animals/robots/virtual
characters. These symmetries arise from the presence of one or more planes/axis
of symmetry in the system's morphology, resulting in harmonious duplication and
distribution of body parts. Significantly, we characterize how morphological
symmetries extend to symmetries in the system's dynamics, optimal control
policies, and in all proprioceptive and exteroceptive measurements related to
the system's dynamics evolution. In the context of data-driven methods,
symmetry represents an inductive bias that justifies the use of data
augmentation or symmetric function approximators. To tackle this, we present a
theoretical and practical framework for identifying the system's morphological
symmetry group \G and characterizing the symmetries in proprioceptive and
exteroceptive data measurements. We then exploit these symmetries using data
augmentation and \G-equivariant neural networks. Our experiments on both
synthetic and real-world applications provide empirical evidence of the
advantageous outcomes resulting from the exploitation of these symmetries,
including improved sample efficiency, enhanced generalization, and reduction of
trainable parameters.Comment: 8 pages, 4 figures, 7 optional appendix pages, 4 appendix figure
An adaptable approach to learn realistic legged locomotion without examples
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Learning controllers that reproduce legged locomotion in nature has been a long-time goal in robotics and computer graphics. While yielding promising results, recent approaches are not yet flexible enough to be applicable to legged systems of different morphologies. This is partly because they often rely on precise motion capture references or elaborate learning environments that ensure the naturality of the emergent locomotion gaits but prevent generalization. This work proposes a generic approach for ensuring realism in locomotion by guiding the learning process with the spring-loaded inverted pendulum model as a reference. Leveraging on the exploration capacities of Reinforcement Learning (RL), we learn a control policy that fills in the information gap between the template model and full-body dynamics required to maintain stable and periodic locomotion. The proposed approach can be applied to robots of different sizes and morphologies and adapted to any RL technique and control architecture. We present experimental results showing that even in a model-free setup and with a simple reactive control architecture, the learned policies can generate realistic and energy-efficient locomotion gaits for a bipedal and a quadrupedal robot. And most importantly, this is achieved without using motion capture, strong constraints in the dynamics or kinematics of the robot, nor prescribing limb coordination. We provide supplemental videos for qualitative analysis of the naturality of the learned gaits.Peer ReviewedPostprint (author's final draft