Transcript Slides
Keyframe-based Learning from Demonstration Anthony Dubis “Keyframe-based Learning from Demonstration – Method and Evaluation” – Akgun, Cakmak, Jiang, and Thomaz What We’ll Cover 1. Learning from Demonstration & Its Types 2. The Proposed Framework 3. The Framework Results and Conclusions Bonus: Video on extension LEARNING FROM DEMONSTRATION & ITS TYPES Learning from Demonstration • Teach a robot through successful examples • Various options – Teleoperation – Motion capture – Kinesthetic manipulation • Paper’s focus: Kinesthetic teaching: Having a human teacher physically guide the robot in performing a skill Two Kinesthetic Input Methods for Demonstrations • Draw Letters Using a Mouse (2D) • Teaching a robot: – Scoop – Pour – Place Learning from Demonstration Introduction (Traditional) • Learning from Demonstration (LfD) • Continuous trajectory with two endpoints • Trajectory Demonstration (TD) Example Learning from Demonstration Advantages • Intuitiveness • No correspondence problem • No extra instrumentation Learning from Demonstration Disadvantages • Users lack experience manipulating robots • Noisy movements Keyframe-based Learning from Demonstration • Keyframe-based Learning from Demonstration (KLfD) • Sparse set of consecutive poses, or critical points • Provide start, end, and several in-between • Keyframe Demonstration (KD) Example Keyframe-based Learning from Demonstration - Advantages • Intuitive for the user • Pick poses with care Keyframe-based Learning from Demonstration - Disadvantages • User lack of experience in manipulating robots • Lack of timing information • Complex and curvy movements are difficult to express Hybrid Learning from Demonstration • Hybrid Learning from Demonstration (HLfD) • Let the user choose whatever suits the situation • Hybrid Demonstration (HD) - Example Demonstration Types • Trajectory, Keyframe, or Hybrid Demonstrations • Convert this data into a Sequential Pose Distribution for skill reproduction KLfD – Proposed Framework • Traditional LfD techniques are limited • Goal: Create one that can take in TD, KD, HD Implementation Overview Can accept and process input from trajectory, keyframe, or hybrid demonstrations. KLfD – Framework Implementation Overview • Trajectory to Keyframe Conversion • Temporal Alignment and Clustering – Provides Sequential Pose Distribution (SPD) • Skill Reproduction – determine parameters Validity • Requirements – Handle trajectory input as well as conventional methods – “Lost” data • Compare trajectory demos to baseline: – Gaussian Mixture Model (GMM) to fit the data – Gaussian Mixture Regression (GMR) to reproduce the skill – GMM + GMR Drawing Letters • 2D mouse gestures • Allows TD, KD, and HD • Skills: B, D, G, M, O, P • Measurement: alignment cost between generated and goal trajectories KFD GMM Validity - 2D Letters Robot Skills • Simon Robot • 7 DOF arms • 2 DoF torso • 13 DoF head • Scooping using TD • Pouring using TD • Placement using KD Validity - Robot Skills Validity Robot Skills • Similar scooping and pouring weights • KLfD framework is on par with GMM+GMR FRAMEWORK RESULTS & CONCLUSIONS Results – Letter Drawing Comparing Input Types Letter O is all curved, trajectory is best. Letter M is straight, KFD is best Conclusions - Advantages • Framework seems to do its job • Stacks up against conventional models • Accept any of the three inputs to create Sequential Pose Distributions (SPD) Conclusions - Disadvantages • Keyframe inputs -> missing velocity parameters • Zero velocity and acceleration assumption Extensions • Adding Queries by robot • PR2