Terrain-adaptive locomotion skills using deep reinforcement learning

193

103

Inverse kinematics (IK) is the use of kinematic equations to determine the joint parameters of a manipulator so that the end effector moves to a desired position; IK can be applied in many areas, including robotics, engineering, computer graphics and video games. In this survey, we present a comprehensive review of the IK problem and the solutions developed over the years from the computer graphics point of view. The paper starts with the definition of forward and IK, their mathematical formulations and explains how to distinguish the unsolvable cases, indicating when a solution is available. The IK literature in this report is divided into four main categories: the analytical, the numerical, the data‐driven and the hybrid methods. A timeline illustrating key methods is presented, explaining how the IK approaches have progressed over the years. The most popular IK methods are discussed with regard to their performance, computational cost and the smoothness of their resulting postures, while we suggest which IK family of solvers is best suited for particular problems. Finally, we indicate the limitations of the current IK methodologies and propose future research directions.

Section: Discussionmentioning

confidence: 99%

Inverse Kinematics Techniques in Computer Graphics: A Survey

Aristidou

Lasenby

Chrysanthou

et al. 2017

193

103

“…Finally, the current framework does not utilize any machine learning techniques. The reinforcement and other learning approaches with well‐specified reward functions can learn robust control policies from a broad range of kinematic reference motions [PALvdP18, PBYVDP17, LH17, LPY16]. However, motion capture for sports with vast moving space in the outdoor environment, such as skiing, snowboarding and waterskiing, is usually not feasible.…”

Section: Resultsmentioning

confidence: 99%

Skiing Simulation Based on Skill‐Guided Motion Planning

Lee

Liou

et al. 2019

Skiing is a popular recreational sport, and competitive skiing has been events at the Winter Olympic Games. Due to its wide moving range in the outdoor environment, motion capture of skiing is hard and usually not a good solution for generating skiing animations. Physical simulation offers a more viable alternative. However, skiing simulation is challenging as skiing involves many complicated motor skills and physics, such as balance keeping, movement coordination, articulated body dynamics and ski‐snow reaction. In particular, as no reference motions — usually from MOCAP data — are readily available for guiding the high‐level motor control, we need to synthesize plausible reference motions additionally. To solve this problem, sports techniques are exploited for reference motion planning. We propose a physics‐based framework that employs kinetic analyses of skiing techniques and the ski–snow contact model to generate realistic skiing motions. By simulating the inclination, angulation and weighting/unweighting techniques, stable and plausible carving turns and bump skiing animations can be generated. We evaluate our framework by demonstrating various skiing motions with different speeds, curvature radii and bump sizes. Our results show that employing the sports techniques used by athletes can provide considerable potential to generate agile sport motions without reference motions.

“…Kang et al [KL17] used motion capture data, but they changed the end‐effector positions to contact with the complex environment properly. Peng et al [PBYVDP17, PALvdP18] mimicked motion capture reference data using DRL.…”

Section: Related Workmentioning

confidence: 99%

Figure Skating Simulation from Video

Park

Lee

2019

Figure skating is one of the most popular ice sports at the Winter Olympic Games. The skaters perform several skating skills to express the beauty of the art on ice. Skating involves moving on ice while wearing skate shoes with thin blades; thus, it requires much practice to skate without losing balance. Moreover, figure skating presents dynamic moves, such as jumping, artistically. Therefore, demonstrating figure skating skills is even more difficult to achieve than basic skating, and professional skaters often fall during Winter Olympic performances. We propose a system to demonstrate figure skating motions with a physically simulated human‐like character. We simulate skating motions with non‐holonomic constraints, which make the skate blade glide on the ice surface. It is difficult to obtain reference motions from figure skaters because figure skating motions are very fast and dynamic. Instead of using motion capture data, we use key poses extracted from videos on YouTube and complete reference motions using trajectory optimization. We demonstrate figure skating skills, such as crossover, three‐turn, and even jump. Finally, we use deep reinforcement learning to generate a robust controller for figure skating skills.