A Sampling-based Motion Planning Framework for Complex Motor Actions

We present a framework for planning complex motor actions such as pouring or scooping from arbitrary start states in cluttered real-world scenes. Traditional approaches to such tasks use dynamic motion primitives (DMPs) learned from human demonstrations. We enhance a recently proposed state-of-the-art DMP technique capable of obstacle avoidance by including them within a novel hybrid framework. This complements DMPs with sampling-based motion planning algorithms, using the latter to explore the scene and reach promising regions from which a DMP can successfully complete the task. Experiments indicate that even obstacle-aware DMPs suffer in task success when used in scenarios which largely differ from the trained demonstration in terms of the start, goal, and obstacles. Our hybrid approach significantly outperforms obstacle-aware DMPs by successfully completing tasks in cluttered scenes for a pouring task in simulation. We further demonstrate our method on a real robot for pouring and scooping tasks.

Authors

• Shlok Sobti
• Rahul Shome
• Swarat Chaudhuri
• Lydia E. Kavraki

Keywords

• Heuristic algorithms
• Dynamics
• Planning
• Task analysis
• Collision avoidance
• Intelligent robots
• Path Planning
• Planning Framework
• Complex Motor Actions
• Sampling-based Motion
• Sampling-based Motion Planning
• Activity Of Complex
• Task Success
• Obstacle Avoidance
• Starting State
• Planning Algorithm
• Hybrid Framework
• Motion Primitives
• Computation Time
• Parametrized
• Robotic Arm
• Tree Search
• Object Task
• Complex Modulation
• Dynamic Time Warping
• Motion Sequences
• End-effector Pose
• Cluttered Environments
• Solution Trajectory