METHODS AND SYSTEMS FOR ROBOT KEYFRAMING AND LEARNING LOCOMOTION WITH HIGH-LEVEL OBJECTIVES

Number: 20260175414
Published: 2026-06-25
Filed: 2024-12-19
Assignee: DISNEY ENTERPRISES, INC.
Inventors: Zargarbashi; Fatemeh, Sumner; Robert Walker, Cheng; Jin, Kang; Dong Ho, Coros; Stelian
CPC: B25J9/163; G05B13/0265
Verdict: High Hardware
First reported: 2026-W29 (2026-07-15)
Source: Google Patents · FreePatentsOnline

The keeper's note

Methods and systems for animated figure keyframing or physical robot keyframing and learning locomotion with high-level objectives are discussed herein.

Abstract

Methods and systems for animated figure keyframing or physical robot keyframing and learning locomotion with high-level objectives are discussed herein. For example, generating motion for an animated figure may include generating a control policy for the animated figure using a reinforcement learning model, wherein the control policy is configured to control a movement of the animated figure to achieve one or more keyframes. Generating motion for the animated figure may further include encoding the control policy onto a processor of the animated figure. In some cases, the control policy for the animated figure may be generated using a multi-input single-output transformer encoder. Generating motion for the animated figure further includes determining one or more target keyframes and generating, using the control policy, the motion for the animated figure based on the one or more target keyframes.

Background

FIELD

The present disclosure relates generally to systems and methods for generating motion for an animated figure. BACKGROUND

Amusement parks, theme parks, carnivals, arcades, and various attractions use animated figures to produce an interactive effect for guests in entertainment experiences. For example, in rides, shows, games, etc. animated figures mimic the movement, look, and emotion of characters in the experience.

Additionally, reinforcement learning has been increasingly applied to develop locomotion policies for, for example, four-legged or two-legged animated figures (e.g., robots and animatronics). The primary focus has been to achieve robust control policies that can accurately track velocity commands from joysticks. More recently, researchers have attempted to enhance the versatility of legged robot controllers by incorporating high-level objectives, particularly through position- or orientation-based targets. This high-level control is typically accomplished through hierarchical frameworks, where a high-level policy is learned to drive a low-level controller. Conversely, end-to-end approaches aim to develop a unified policy for both high- and low-level control, allowing high-level objectives to directly influence low-level decisions. However, the current implementations of reinforcement learning urge the animated figure to reach a target as fast as possible, lacking refined control of the timing for achieving the target.

Current anim

Claims

1. A method of generating motion for an animated figure comprising: generating a control policy for the animated figure using a reinforcement learning model, wherein the control policy is configured to control a movement of the animated figure to achieve one or more keyframes; encoding the control policy onto a processor of the animated figure; receiving one or more target keyframes; and generating, using the control policy, the motion for the animated figure based on the one or more target keyframes. || 10. An animated figure comprising: at least one actuator: a processing element; a memory component, wherein the memory component stores a control policy trained based on one or more sparse rewards and one or more dense rewards, wherein the one or more dense rewards and the one or more sparse rewards are based on one or more target keyframes of the animated figure. || 15. A non-transitory computer-readable media comprising instructions to cause an animated figure to: receive one or more target keyframes; process, using a multi-input single-output transformer encoder, at least one or more sparse rewards and at least one or more dense rewards corresponding to the one or more target keyframes; and generate motion for the animated figure based on an output of the multi-input single-output transformer encoder, wherein the motion achieves the one or more target keyframes.