16 research outputs found
Guided Curriculum Learning for Walking Over Complex Terrain
Reliable bipedal walking over complex terrain is a challenging problem, using
a curriculum can help learning. Curriculum learning is the idea of starting
with an achievable version of a task and increasing the difficulty as a success
criteria is met. We propose a 3-stage curriculum to train Deep Reinforcement
Learning policies for bipedal walking over various challenging terrains. In the
first stage, the agent starts on an easy terrain and the terrain difficulty is
gradually increased, while forces derived from a target policy are applied to
the robot joints and the base. In the second stage, the guiding forces are
gradually reduced to zero. Finally, in the third stage, random perturbations
with increasing magnitude are applied to the robot base, so the robustness of
the policies are improved. In simulation experiments, we show that our approach
is effective in learning walking policies, separate from each other, for five
terrain types: flat, hurdles, gaps, stairs, and steps. Moreover, we demonstrate
that in the absence of human demonstrations, a simple hand designed walking
trajectory is a sufficient prior to learn to traverse complex terrain types. In
ablation studies, we show that taking out any one of the three stages of the
curriculum degrades the learning performance.Comment: Submitted to Australasian Conference on Robotics and Automation
(ACRA) 202
When and Where to Step: Terrain-Aware Real-Time Footstep Location and Timing Optimization for Bipedal Robots
Online footstep planning is essential for bipedal walking robots, allowing
them to walk in the presence of disturbances and sensory noise. Most of the
literature on the topic has focused on optimizing the footstep placement while
keeping the step timing constant. In this work, we introduce a footstep planner
capable of optimizing footstep placement and step time online. The proposed
planner, consisting of an Interior Point Optimizer (IPOPT) and an optimizer
based on Augmented Lagrangian (AL) method with analytical gradient descent,
solves the full dynamics of the Linear Inverted Pendulum (LIP) model in real
time to optimize for footstep location as well as step timing at the rate of
200~Hz. We show that such asynchronous real-time optimization with the AL
method (ARTO-AL) provides the required robustness and speed for successful
online footstep planning. Furthermore, ARTO-AL can be extended to plan
footsteps in 3D, allowing terrain-aware footstep planning on uneven terrains.
Compared to an algorithm with no footstep time adaptation, our proposed ARTO-AL
demonstrates increased stability in simulated walking experiments as it can
resist pushes on flat ground and on a ramp up to 120 N and 100 N
respectively. For the video, see https://youtu.be/ABdnvPqCUu4. For code, see
https://github.com/WangKeAlchemist/ARTO-AL/tree/master.Comment: 32 pages, 15 figures. Submitted to Robotics and Autonomous System
A survey on human performance capture and animation
With the rapid development of computing technology, three-dimensional (3D) human body
models and their dynamic motions are widely used in the digital entertainment industry. Human perfor-
mance mainly involves human body shapes and motions. Key research problems include how to capture
and analyze static geometric appearance and dynamic movement of human bodies, and how to simulate
human body motions with physical e�ects. In this survey, according to main research directions of human body performance capture and animation, we summarize recent advances in key research topics, namely
human body surface reconstruction, motion capture and synthesis, as well as physics-based motion sim-
ulation, and further discuss future research problems and directions. We hope this will be helpful for
readers to have a comprehensive understanding of human performance capture and animatio
Neuroevolution of Actively Controlled Virtual Characters
Master's thesis Information- and communication technology IKT590 - University of Agder 2017Physics-based character animation offer an attractive alternative to traditional animation
techniques, however, physics-based approaches often struggle to incorporate active user
control of these characters. This thesis suggests a different approach to the problem of
actively controlled virtual characters. The proposed solution takes a neuroevolutionary
approach, using HyperNEAT to evolve neural controllers for a simulated eight-legged
character, a previously untested character morphology for this algorithm. Using these
controllers this thesis aims to evaluate the robustness and responsiveness of a control
strategy that changes between them based on simulated user input. The results show that
HyperNEAT is quite capable of evolving long walking controllers for this character, but
also suggests a need for further refinement when operated in tandem
Interactive avatar control: Case studies on physics and performance based character animation
Master'sMASTER OF SCIENC